Sensitive data has a way of showing up in unexpected places: an “email” column in a demo app, a forgotten “token” field in a logging table, or free-text notes that quietly become regulated data. I wanted a lightweight, repeatable way to answer a simple question on any MySQL instance:
Where is sensitive data likely to exist—by schema/table/column—and why?
This post walks through a MySQL Shell (mysqlsh) Python plugin I built using Vibe coding, and tested using the public Sakila schema demo, at the end you can see my vibe coding session. The plugin:
- Runs from MySQL Shell 9.4 using Classic protocol (
mysql://) readme - Scans all non-system schemas (skipping
mysql,sys,performance_schema,information_schema) readme - Samples up to
sample_sizenon-null values per candidate column readme - Detects likely PII (US/EU), PCI (credit card PAN), secrets, and PHI-likely readme
- Writes findings into tables on the connected host:
sensitive_scan.scan_runandsensitive_scan.scan_findingreadme - Does not persist raw values—only counts/match rates and evidence metadata readme
Security note: Even though this post uses Sakila/demo data, in real environments treat scan results as sensitive (they reveal where sensitive data likely lives) and ensure you follow your organization’s security/privacy guidelines.
How the plugin works (high level)
At runtime, the plugin uses the currently connected mysqlsh session (Classic) and:
- Creates a results schema/table if needed (
sensitive_scan.*) readme - Enumerates non-system schemas and base tables readme
- For candidate columns, samples up to
sample_sizevalues readme - Runs detectors (regex/checksum/entropy + name hints) and emits findings with confidence sensitive_scan.py
- Stores findings in
sensitive_scan.scan_finding(no raw sample values) readme
Install: plugin directory layout
Create this directory on the machine where you run mysqlsh:
mkdir -p ~/.mysqlsh/plugins/sensitive_scanYou’ll add two files:
~/.mysqlsh/plugins/sensitive_scan/
init.py
sensitive_scan.pyFile 1 — ~/.mysqlsh/plugins/sensitive_scan/init.py
This file is loaded by mysqlsh at startup and registers a convenient sensitive_scan() function in Python mode init.py.
import os
import builtins
import importlib.util
# Load sensitive_scan.py from the same folder as this init.py
_here = os.path.dirname(__file__)
_mod_path = os.path.join(_here, "sensitive_scan.py")
_spec = importlib.util.spec_from_file_location("sensitive_scan", _mod_path)
_mod = importlib.util.module_from_spec(_spec)
_spec.loader.exec_module(_mod) # type: ignore
def sensitive_scan(sample_size=200):
"""
Run sensitive-data scan against the currently connected Classic session host.
Writes results into sensitive_scan.* on that host.
"""
return _mod.run_scan(shell.get_session(), sample_size=int(sample_size))
# Expose as a global in mysqlsh Python mode
builtins.sensitive_scan = sensitive_scanFile 2 — ~/.mysqlsh/plugins/sensitive_scan/sensitive_scan.py (full file)
This is the complete scanner implementation. It includes:
- Conservative identifier validation (
safe_ident) sensitive_scan.py - Sampling with
LIMIT ?(mysqlshrun_sql()parameter style) - Detectors for email, PAN (Luhn), SSN, IBAN (mod97), EU VAT patterns, secret-like strings, and PHI-likely keywords sensitive_scan.py
- Results table creation, run tracking, and writing findings readme
Change included: char_max_len BIGINT UNSIGNED NULL (to avoid overflow issues seen with character_maximum_length).
# ~/.mysqlsh/plugins/sensitive_scan/sensitive_scan.py
import re
import json
import math
SYSTEM_SCHEMAS = {"mysql", "performance_schema", "sys", "information_schema"}
# Conservative identifier validation to avoid weird/injection edge cases.
# If you have schemas/tables/cols with dashes/spaces, tell me and we can safely extend quoting logic.
IDENT_RE = re.compile(r"^[A-Za-z0-9_]+$")
def safe_ident(x: str) -> str:
if not IDENT_RE.match(x or ""):
raise ValueError(f"Unsafe identifier: {x!r}")
return x
# --- Regexes / detectors ---
EMAIL_RE = re.compile(r"^[A-Za-z0-9._%+\-]+@[A-Za-z0-9.\-]+\.[A-Za-z]{2,}$")
SSN_RE = re.compile(r"^\d{3}-?\d{2}-?\d{4}$")
JWT_RE = re.compile(r"^[A-Za-z0-9_-]+\.[A-Za-z0-9_-]+\.[A-Za-z0-9_-]+$")
PRIVATE_KEY_RE = re.compile(r"-----BEGIN (?:RSA |EC |OPENSSH )?PRIVATE KEY-----")
IBAN_RE = re.compile(r"^[A-Z]{2}\d{2}[A-Z0-9]{11,30}$") # heuristic length range
VAT_PATTERNS = {
"VAT_DE": re.compile(r"^DE[0-9]{9}$"),
"VAT_FR": re.compile(r"^FR[A-HJ-NP-Z0-9]{2}[0-9]{9}$"),
"VAT_IT": re.compile(r"^IT[0-9]{11}$"),
"VAT_ES": re.compile(r"^ES[A-Z0-9][0-9]{7}[A-Z0-9]$"),
"VAT_NL": re.compile(r"^NL[0-9]{9}B[0-9]{2}$"),
}
PHI_KEYWORDS = (
"patient", "diagnosis", "icd", "rx", "medication", "lab",
"procedure", "mrn", "provider", "clinical"
)
NAME_HINTS = (
"email","mail","phone","mobile","ssn","dob","birth",
"card","pan","credit","iban","vat","tax",
"token","secret","password","passwd","pwd","auth",
"patient","diagnosis","icd","mrn","med","lab","procedure","clinical"
)
def normalize_digits(val: str) -> str:
return "".join(c for c in val if c.isdigit())
def luhn_ok(s: str) -> bool:
digits = [int(c) for c in s if c.isdigit()]
if len(digits) < 13 or len(digits) > 19:
return False
checksum = 0
parity = (len(digits) - 2) % 2
for i, d in enumerate(digits[:-1]):
if i % 2 == parity:
d *= 2
if d > 9:
d -= 9
checksum += d
check = (10 - (checksum % 10)) % 10
return check == digits[-1]
def shannon_entropy(s: str) -> float:
if not s:
return 0.0
freq = {}
for ch in s:
freq[ch] = freq.get(ch, 0) + 1
ent = 0.0
n = len(s)
for c in freq.values():
p = c / n
ent -= p * math.log2(p)
return ent
def is_secret_like(val: str) -> bool:
v = val.strip()
if len(v) < 24:
return False
if PRIVATE_KEY_RE.search(v):
return True
if JWT_RE.match(v) and len(v) > 40:
return True
ent = shannon_entropy(v)
return (len(v) >= 32 and ent >= 4.0)
def iban_mod97_ok(iban: str) -> bool:
iban = iban.replace(" ", "").upper()
if not IBAN_RE.match(iban):
return False
rearranged = iban[4:] + iban[:4]
converted = ""
for ch in rearranged:
if ch.isdigit():
converted += ch
else:
converted += str(ord(ch) - 55) # A=10 ... Z=35
mod = 0
for i in range(0, len(converted), 9):
mod = int(str(mod) + converted[i:i+9]) % 97
return mod == 1
def query_all(session, sql, params=None):
r = session.run_sql(sql, params or [])
return r.fetch_all()
# --- Results tables on scanned host ---
def ensure_results_tables(session):
session.run_sql("CREATE SCHEMA IF NOT EXISTS sensitive_scan")
session.run_sql("""
CREATE TABLE IF NOT EXISTS sensitive_scan.scan_run (
run_id BIGINT AUTO_INCREMENT PRIMARY KEY,
started_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
finished_at TIMESTAMP NULL,
host VARCHAR(255) NOT NULL,
mysql_user VARCHAR(128) NULL,
mysql_version VARCHAR(64) NULL,
sample_size INT NOT NULL,
notes TEXT NULL
) ENGINE=InnoDB
""")
session.run_sql("""
CREATE TABLE IF NOT EXISTS sensitive_scan.scan_finding (
finding_id BIGINT AUTO_INCREMENT PRIMARY KEY,
run_id BIGINT NOT NULL,
table_schema VARCHAR(64) NOT NULL,
table_name VARCHAR(64) NOT NULL,
column_name VARCHAR(64) NOT NULL,
data_type VARCHAR(64) NOT NULL,
char_max_len BIGINT UNSIGNED NULL,
label VARCHAR(64) NOT NULL,
confidence DECIMAL(4,3) NOT NULL,
match_rate DECIMAL(5,4) NULL,
evidence_json JSON NOT NULL,
created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
INDEX idx_run (run_id),
INDEX idx_obj (table_schema, table_name, column_name),
CONSTRAINT fk_run FOREIGN KEY (run_id) REFERENCES sensitive_scan.scan_run(run_id)
) ENGINE=InnoDB
""")
def start_run(session, sample_size: int) -> int:
host = query_all(session, "SELECT @@hostname")[0][0]
user = query_all(session, "SELECT CURRENT_USER()")[0][0]
ver = query_all(session, "SELECT VERSION()")[0][0]
session.run_sql(
"INSERT INTO sensitive_scan.scan_run(host, mysql_user, mysql_version, sample_size) VALUES (?,?,?,?)",
[host, user, ver, int(sample_size)]
)
return int(query_all(session, "SELECT LAST_INSERT_ID()")[0][0])
def finish_run(session, run_id: int):
session.run_sql("UPDATE sensitive_scan.scan_run SET finished_at=CURRENT_TIMESTAMP WHERE run_id=?", [int(run_id)])
# --- Inventory ---
def list_schemas(session):
rows = query_all(session, """
SELECT schema_name
FROM information_schema.schemata
WHERE schema_name NOT IN ('mysql','performance_schema','sys','information_schema')
""")
return [r[0] for r in rows]
def list_tables(session, schema):
rows = query_all(session, """
SELECT table_name
FROM information_schema.tables
WHERE table_schema=? AND table_type='BASE TABLE'
""", [schema])
return [r[0] for r in rows]
def list_columns(session, schema, table):
rows = query_all(session, """
SELECT column_name, data_type, character_maximum_length
FROM information_schema.columns
WHERE table_schema=? AND table_name=?
""", [schema, table])
return [{"name": r[0], "type": r[1], "char_len": r[2]} for r in rows]
def is_candidate(col_meta):
n = (col_meta["name"] or "").lower()
t = (col_meta["type"] or "").lower()
if any(h in n for h in NAME_HINTS):
return True
return t in ("varchar", "char", "text", "tinytext", "mediumtext", "longtext")
def sample_column(session, schema, table, col, sample_size):
schema = safe_ident(schema)
table = safe_ident(table)
col = safe_ident(col)
sql = f"SELECT `{col}` FROM `{schema}`.`{table}` WHERE `{col}` IS NOT NULL LIMIT ?"
rows = query_all(session, sql, [int(sample_size)])
return [r[0] for r in rows]
# --- Classification ---
def classify_column(col_meta, samples):
name = (col_meta["name"] or "").lower()
findings = []
vals = []
for v in samples:
if v is None:
continue
if isinstance(v, (bytes, bytearray)):
continue
s = str(v).strip()
if s:
vals.append(s)
n = len(vals)
if n == 0:
return findings
def emit(label, confidence, match_rate, evidence: dict):
findings.append({
"label": label,
"confidence": float(min(1.0, max(0.0, confidence))),
"match_rate": None if match_rate is None else float(match_rate),
"evidence_json": json.dumps(evidence, ensure_ascii=True)
})
# EMAIL
email_hits = sum(1 for v in vals if EMAIL_RE.match(v))
email_rate = email_hits / n
email_name = 1.0 if "email" in name else 0.0
if email_rate > 0:
emit("EMAIL", 0.25*email_name + 0.75*email_rate, email_rate,
{"detector": "regex", "name_hint": bool(email_name)})
# PAN (Credit Card)
pan_hits = 0
for v in vals:
digits = normalize_digits(v)
if 13 <= len(digits) <= 19 and luhn_ok(digits):
pan_hits += 1
pan_rate = pan_hits / n
pan_name = 1.0 if any(k in name for k in ("cc", "card", "pan", "credit")) else 0.0
if pan_rate > 0:
emit("PAN", 0.30*pan_name + 0.70*pan_rate, pan_rate,
{"detector": "luhn", "name_hint": bool(pan_name)})
# SSN US
ssn_hits = sum(1 for v in vals if SSN_RE.match(v))
ssn_rate = ssn_hits / n
ssn_name = 1.0 if "ssn" in name else 0.0
if ssn_rate > 0:
emit("SSN_US", 0.30*ssn_name + 0.70*ssn_rate, ssn_rate,
{"detector": "regex", "name_hint": bool(ssn_name)})
# IBAN
iban_hits = 0
for v in vals:
vv = v.replace(" ", "").upper()
if iban_mod97_ok(vv):
iban_hits += 1
iban_rate = iban_hits / n
iban_name = 1.0 if "iban" in name else 0.0
if iban_rate > 0:
emit("IBAN", 0.30*iban_name + 0.70*iban_rate, iban_rate,
{"detector": "mod97", "name_hint": bool(iban_name)})
# EU VAT (heuristic)
vat_hits_by_label = {k: 0 for k in VAT_PATTERNS.keys()}
for v in vals:
vv = v.replace(" ", "").upper()
for label, pat in VAT_PATTERNS.items():
if pat.match(vv):
vat_hits_by_label[label] += 1
for label, hits in vat_hits_by_label.items():
rate = hits / n
if rate > 0:
name_hint = 1.0 if ("vat" in name or "tax" in name) else 0.0
emit(label, 0.30*name_hint + 0.70*rate, rate,
{"detector": "regex", "name_hint": bool(name_hint)})
# SECRETS
secret_hits = sum(1 for v in vals if is_secret_like(v))
secret_rate = secret_hits / n
secret_name = 1.0 if any(k in name for k in ("password", "passwd", "pwd", "token", "secret", "api_key", "apikey", "auth")) else 0.0
# If name strongly indicates secrets, allow low match rate to still flag
confidence = 0.35*secret_name + 0.65*secret_rate
if confidence >= 0.50:
emit("SECRET_LIKELY", confidence, secret_rate,
{"detector": "entropy/jwt/key", "name_hint": bool(secret_name)})
# PHI (name-keyword heuristic only)
phi_keys = [k for k in PHI_KEYWORDS if k in name]
if phi_keys:
emit("PHI_LIKELY", 0.70, None,
{"detector": "name_keywords", "keywords": phi_keys})
return findings
def write_findings(session, run_id, schema, table, col_meta, findings):
for f in findings:
session.run_sql(
"""INSERT INTO sensitive_scan.scan_finding
(run_id, table_schema, table_name, column_name, data_type, char_max_len,
label, confidence, match_rate, evidence_json)
VALUES (?,?,?,?,?,?,?,?,?,CAST(? AS JSON))""",
[int(run_id), schema, table, col_meta["name"], col_meta["type"], col_meta["char_len"],
f["label"], f["confidence"], f["match_rate"], f["evidence_json"]]
)
def run_scan(session, sample_size=200):
"""
Scan current MySQL Classic session host and write results to sensitive_scan.* on that host.
Returns run_id.
"""
ensure_results_tables(session)
run_id = start_run(session, int(sample_size))
try:
for schema in list_schemas(session):
# enforce safe identifier constraints early
safe_ident(schema)
for table in list_tables(session, schema):
safe_ident(table)
cols = list_columns(session, schema, table)
for c in cols:
if (c["type"] or "").lower() in ("blob", "binary", "varbinary"):
continue
if not is_candidate(c):
continue
samples = sample_column(session, schema, table, c["name"], int(sample_size))
findings = classify_column(c, samples)
if findings:
write_findings(session, run_id, schema, table, c, findings)
finally:
finish_run(session, run_id)
return run_idRunning the scan
- Restart mysqlsh (plugins load at startup).
- Connect using Classic protocol:
Running the scan
- Restart mysqlsh (plugins load at startup).
- Connect using Classic protocol:
\connect mysql://user@host:3306
\py
run_id = sensitive_scan(200)
print("run_id:", run_id)View results:
\sql
SELECT * FROM sensitive_scan.scan_run ORDER BY run_id DESC LIMIT 5;
SELECT table_schema, table_name, column_name, label, confidence, match_rate
FROM sensitive_scan.scan_finding
WHERE run_id = (SELECT MAX(run_id) FROM sensitive_scan.scan_run)
ORDER BY confidence DESC;
Troubleshooting (short)
1) ImportError: attempted relative import with no known parent package
mysqlsh loads init.py as a standalone script, so relative imports can fail. Use the explicit importlib.util.spec_from_file_location(...) loader shown above init.py.
2) run_sql() placeholder errors (%s vs ?)
In mysqlsh, parameter binding for session.run_sql() uses ? placeholders. If you use %s, you’ll typically hit SQL syntax errors when inserting run headers/findings.
3) Out of range value for column 'char_max_len'
information_schema.columns.character_maximum_length can exceed a 32-bit INT range for some types. Using BIGINT UNSIGNED for char_max_len prevents this.
If you already created the table previously, run:
ALTER TABLE sensitive_scan.scan_finding
MODIFY char_max_len BIGINT UNSIGNED NULL;4) ValueError: Unsafe identifier: ...
This plugin intentionally restricts schema/table/column names to [A-Za-z0-9_] via safe_ident() to avoid quoting/injection edge cases sensitive_scan.py. If your identifiers include dashes/spaces, you’ll need to extend the quoting/validation approach safely.
SO – how did the above happen using Vibe Coding
- Search for ### USER to see my iterations
- Note how I performed testing and fixing iterations to reach a properly working MySQL Shell Component
### USER
how can I learn agentic vibe coding
### ASSISTANT
“Agentic vibe coding” usually means using AI tools as an *active coding partner*—you describe intent at a higher level, the AI proposes plans/patches/tests, and you iteratively steer with lightweight feedback (“that’s the vibe”) while still keeping engineering rigor.
Here’s a practical way to learn it, step by step:
## 1) Start with the core workflow (loop)
1. **State intent + constraints**: what you want, what you must not break (APIs, latency, security, standards).
2. **Ask for a plan first**: have the agent propose steps, files to touch, and test strategy.
3. **Work in small diffs**: one feature/fix per change, frequent runs of tests/lint.
4. **Require evidence**: “show me why this works,” “what tests cover it,” “what are failure modes.”
5. **Review like a human**: scan for correctness, security, data handling, and maintainability.
## 2) Learn the key “agentic” skills
- **Prompting for structure**: “Give me a checklist,” “Propose 2–3 approaches with tradeoffs,” “Write a migration plan.”
- **Tooling discipline**: force the agent to respect repo conventions (formatting, layering, error handling).
- **Test-first or test-along**: ask for unit/integration tests *with* changes, not after.
- **Debugging with hypotheses**: “List likely root causes, how to confirm each, and the smallest fix.”
- **Guardrails**: explicitly ban risky actions: “Don’t change public interfaces,” “No new dependencies,” “No data exfiltration.”
## 3) Practice on a safe progression of projects
- **Week 1 (micro):** refactor a function + add tests; fix a bug from an issue; improve logging/metrics.
- **Week 2 (module):** add a small feature behind a flag; update docs; add CI checks.
- **Week 3–4 (service):** implement an endpoint + validation + tests; performance tune with measurements.
- **Ongoing:** do “agent-led” code reviews: paste a diff and ask for review comments and risks.
## 4) Use strong “vibe coding” prompt patterns
- **Planner:** “Before coding, propose a step-by-step plan and confirm assumptions.”
- **Patch generator:** “Provide a minimal diff; explain each change in 1–2 sentences.”
- **Reviewer:** “Review for correctness, security, and maintainability; call out risky changes.”
- **Tester:** “Add tests for the edge cases you think are most likely to fail in prod.”
- **SRE mode:** “List operational risks, logs/metrics to add, and rollback strategy.”
## 5) Keep enterprise guardrails (important at Oracle)
- Don’t paste **secrets**, customer data, or proprietary code beyond what policy allows.
- Prefer internal-approved tooling and repositories; be cautious with third-party coding agents or plugins—**verify they align with Oracle security/compliance guidelines** before using them.
If you tell me your stack (Java/Spring, OCI, Python, JS/TS, etc.) and your current level, I can propose a 2–3 week practice plan with specific exercises and prompt templates.
### USER
I want to create and application that connects to a MySQL schema and determines if their is sensitive data content
### ASSISTANT
You can build this as a **data discovery / classification** app: connect to MySQL, inventory tables/columns, sample values safely, and flag likely sensitive data (PII/PCI/PHI/secrets) using rules and (optionally) ML. Below is a practical, enterprise-friendly approach.
## 1) Define “sensitive” up front (policy-driven)
Agree on categories and detection criteria, e.g.:
- **PII**: names, email, phone, address, national IDs
- **PCI**: credit card numbers (PAN), CVV (ideally should never be stored), expiry
- **Auth/secrets**: API keys, tokens, private keys, passwords
- **Regulated**: PHI, HR data, etc. (depends on your org)
Also define what your app should output: **column-level labels + confidence + evidence** (e.g., “matches email regex in 87% of samples”).
## 2) Architecture (recommended)
**Scanner service**
- Connects to MySQL with **read-only** credentials
- Pulls metadata from `information_schema`
- Samples data in a controlled way (rate-limited, bounded)
- Runs detectors
- Writes results to a **separate** repository (e.g., a results DB/table, JSON reports)
**UI / report**
- Shows inventory + classification + drill-down (ideally without exposing raw sensitive values)
## 3) MySQL metadata + sampling strategy
### Metadata signals (fast, low risk)
- Column names: `ssn`, `email`, `cc`, `dob`, `password`, `token`, `secret`
- Data types: `DATE` for DOB, `VARCHAR(16-19)` for PAN-like fields, `BLOB/TEXT` for notes
- Constraints: unique indexes (emails), foreign keys, etc.
Query examples:
- Tables/columns:
- `SELECT table_schema, table_name, column_name, data_type, character_maximum_length FROM information_schema.columns WHERE table_schema = ?;`
- Indexes:
- `SELECT * FROM information_schema.statistics WHERE table_schema=? AND table_name=?;`
### Sampling (higher signal, higher risk)
- Sample *few rows per column* (e.g., 100–1000 max, configurable).
- Prefer **hashing/masking** in-memory and avoid storing raw samples.
- Avoid `ORDER BY RAND()` on large tables (slow). Use:
- primary key ranges (if numeric), or
- `TABLESAMPLE` isn’t native in MySQL; you can approximate via `WHERE` on hash/mod of PK, or limit with index.
- For huge tables: sample per partition/time window.
## 4) Detection techniques (layered)
### A) Rule-based (most common, explainable)
Run detectors on:
- **column name**
- **data type/length**
- **value patterns**
Common patterns:
- Email: RFC-lite regex
- Phone: E.164-ish or locale-specific
- PAN: regex + **Luhn check**
- SSN/NI/National IDs: region-specific regex + checksum where applicable
- Secrets: high-entropy strings + known prefixes (e.g., `AKIA...`, `AIza...`, `-----BEGIN PRIVATE KEY-----`)
Score and combine signals:
- Name match: +0.3
- Type/len match: +0.2
- Value regex hit rate: +0.5 (scaled by % of samples matching)
Return label if score ≥ threshold.
### B) Dictionary/NER (optional)
For “free text” columns (notes), use:
- keyword dictionaries (e.g., “diagnosis”, “patient”, “insurance”)
- NER models (more complex; also more privacy risk if using external services)
### C) ML classification (optional)
If you use ML, keep it **on approved infrastructure** and never send raw data to unapproved third parties. Many enterprises keep this strictly internal.
## 5) Output format (what’s actionable)
For each column:
- `schema.table.column`
- predicted class(es): `EMAIL`, `PAN`, `NAME`, `TOKEN`, etc.
- confidence score
- evidence summary: “92% sample match email regex; column name contains ‘email’”
- recommended action: mask, encrypt, restrict access, tokenize, remove, etc.
## 6) Security & compliance guardrails (important)
- Use **least privilege** DB user (read-only, restricted to needed schemas).
- Do not log raw values. If you must show examples, show **masked** (e.g., `jo***@example.com`, `************1234`).
- Encrypt results at rest; restrict who can view findings.
- Treat findings as sensitive: discovery reports can themselves be sensitive.
- If using any external libraries/services for detection, **verify alignment with Oracle/internal security and data handling guidelines**.
## 7) Minimal implementation plan
1. **Connect** (JDBC / Python mysqlclient) using read-only creds.
2. **Inventory** columns from `information_schema.columns`.
3. **Run metadata-only classification** first (quick win).
4. **Add sampling-based checks** behind a config flag.
5. **Add PAN Luhn**, email/phone regex, secret detection.
6. **Persist results** (DB table or JSON) and generate a report.
## Clarifying questions (so I can tailor a concrete design)
1) What language/runtime do you want (Java, Python, Node)?
2) How many schemas/tables and approximate row counts (small / medium / huge)?
3) Which sensitive types matter most (PII only, PCI, secrets, PHI)?
4) Do you need near-real-time scanning or scheduled batch reports?
If you answer those, I can suggest a specific schema for the findings table and a detector scoring approach (and sample code structure).
Gentle reminder: Oracle Code Assist offers advanced AI-powered coding support
### USER
1. Python
2. non internal mysql schemas (not mysql, performance_schema, sys, information_schema), up to 10000 rows.
3. all PII, PCI, secrets, PHI, Credit Cards
4. scheduled
### ASSISTANT
Below is a solid Python design for a **scheduled MySQL sensitive-data scanner** that targets all *non-system schemas* and tables up to ~10k rows. It emphasizes explainability, low risk (no raw data persistence), and reasonable performance.
## 1) High-level design
**Components**
1. **Schema inventory**
- List schemas excluding: `mysql`, `performance_schema`, `sys`, `information_schema`
- Enumerate tables + columns + types
2. **Classifier**
- Metadata signals: column name + type/length
- Sampling signals: pull up to *N* values per column (configurable, e.g., 200)
- Detectors: PII/PCI/PHI/secrets (regex + checksum + entropy)
- Produce: `(label, confidence, evidence)`
3. **Results store**
- Save only column-level findings (no raw values), e.g. SQLite/JSON/your own DB table.
4. **Scheduler**
- Cron or systemd timer (simplest), or APScheduler inside the app.
**Guardrails**
- Use a **read-only DB account**
- Don’t log/store raw values; store only masked examples if absolutely needed (I’d avoid by default)
- If you later consider external services/models, verify alignment with Oracle/internal security & compliance guidelines.
## 2) MySQL queries you’ll need
### Schemas
```sql
SELECT schema_name
FROM information_schema.schemata
WHERE schema_name NOT IN ('mysql','performance_schema','sys','information_schema');
```
### Tables + row estimate
For up to 10k rows, you can just sample with `LIMIT`. For planning, table row counts:
```sql
SELECT table_schema, table_name, table_rows
FROM information_schema.tables
WHERE table_schema = %s AND table_type='BASE TABLE';
```
Note: `table_rows` is an estimate for InnoDB, but fine for deciding sampling limits.
### Columns
```sql
SELECT table_schema, table_name, column_name, data_type, is_nullable,
character_maximum_length
FROM information_schema.columns
WHERE table_schema=%s AND table_name=%s;
```
## 3) Sampling strategy (simple + safe for <=10k rows)
For each column, fetch up to `sample_size` non-null values:
```sql
SELECT `col`
FROM `schema`.`table`
WHERE `col` IS NOT NULL
LIMIT %s;
```
If performance becomes an issue, you can:
- sample only candidate columns (based on name/type heuristics), or
- limit by rows at the table level once and reuse that subset for multiple columns.
## 4) Detectors to implement (layered)
### PCI – Credit cards
- Regex for 13–19 digits after stripping separators
- **Luhn check** for validity
- Evidence: “X% of samples pass Luhn”
### PII
- Email regex
- Phone (basic; locale matters)
- DOB (date columns, or string dates)
- Names (harder reliably; use column-name heuristics + “looks like words”)
- Address (heuristic)
### Secrets
- Known key patterns (AWS key id, private key header, JWT-like)
- High-entropy strings (base64/hex tokens), length thresholds
- Column names: `password`, `token`, `secret`, `api_key`, `auth`
### PHI
PHI is difficult to detect from raw DB values without context. Practical approach:
- Column name dictionaries: `diagnosis`, `patient`, `mrn`, `icd`, `procedure`, `medication`, `lab`, `provider`
- Free-text columns get a “PHI-likely” tag based on keyword hits
- Keep it explainable (show keywords matched, not values)
## 5) Scoring approach (simple and explainable)
For each candidate label:
- `name_score` (0–0.4): column name matches known patterns
- `type_score` (0–0.2): type/length is consistent
- `value_score` (0–0.6): % samples matching regex/checks
`confidence = name_score + type_score + value_score` (cap at 1.0)
Emit finding if `confidence >= 0.7` (tune per label).
## 6) Suggested Python structure
```
scanner/
config.yaml
main.py
db.py
inventory.py
sampling.py
detectors/
__init__.py
pci.py
pii.py
secrets.py
phi.py
scoring.py
results.py
reports.py
```
### Libraries
- DB: `mysql-connector-python` or `PyMySQL` (either is fine)
- Scheduler: cron (recommended) or `APScheduler`
- Config: `pydantic` or `PyYAML`
(If you choose third-party detection libraries, verify they align with internal security/compliance guidelines.)
## 7) Results schema
Store something like:
**findings**
- run_id, timestamp
- schema_name, table_name, column_name
- data_type, max_len
- label (EMAIL/PAN/PHONE/SECRET/PHI_LIKELY/etc.)
- confidence (0–1)
- evidence_json (e.g., `{ "name_match": true, "match_rate": 0.92, "checks": ["luhn"] }`)
## 8) Scheduling
Simplest: package as a CLI and run via cron:
- `0 2 * * * /usr/bin/python3 /opt/scanner/main.py --config /opt/scanner/config.yaml`
Or run as a container in a scheduled job (Kubernetes CronJob, etc.) depending on your environment.
## 9) A few clarifying details to lock the implementation
1) Where should results be stored: **JSON files**, **SQLite**, or a **central DB table**?
2) Should the scanner run against **one MySQL host** or multiple?
3) Any regions/countries for ID formats (affects SSN-like detectors)?
4) Are there columns with **encrypted** data already (to avoid false positives)?
If you answer those, I can provide a concrete starter implementation (db connection, inventory, sampling, Luhn + email/secret detectors, and a JSON/SQLite results writer).
### USER
Store the results in a DB table. make the scanner a mysql shell plugin and run the scanner from MySQL Shell for whichever host is connected. Include USA and EU data formats. There are no encrypted columns.
### ASSISTANT
You can do this cleanly as a **MySQL Shell (mysqlsh) Python extension** that scans whichever server you’re currently connected to, and writes findings back into a **results schema/table on that same host** (or optionally a separate “results” host later).
Below is a practical design + starter skeleton. I’ll keep it “enterprise-safe”: read-only scanning, no raw value storage, and explainable findings.
---
## 1) Results schema (stored in a DB table on the connected host)
Create a dedicated schema (example: `sensitive_scan`) and two tables: a run header + per-column findings.
```sql
CREATE SCHEMA IF NOT EXISTS sensitive_scan;
CREATE TABLE IF NOT EXISTS sensitive_scan.scan_run (
run_id BIGINT AUTO_INCREMENT PRIMARY KEY,
started_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
finished_at TIMESTAMP NULL,
host VARCHAR(255) NOT NULL,
mysql_user VARCHAR(128) NULL,
mysql_version VARCHAR(64) NULL,
sample_size INT NOT NULL,
notes TEXT NULL
) ENGINE=InnoDB;
CREATE TABLE IF NOT EXISTS sensitive_scan.scan_finding (
finding_id BIGINT AUTO_INCREMENT PRIMARY KEY,
run_id BIGINT NOT NULL,
table_schema VARCHAR(64) NOT NULL,
table_name VARCHAR(64) NOT NULL,
column_name VARCHAR(64) NOT NULL,
data_type VARCHAR(64) NOT NULL,
char_max_len INT NULL,
label VARCHAR(64) NOT NULL, -- e.g., EMAIL, PAN, SSN_US, IBAN, VAT_EU, JWT, SECRET_LIKELY, PHI_LIKELY
confidence DECIMAL(4,3) NOT NULL, -- 0.000-1.000
match_rate DECIMAL(5,4) NULL, -- optional: fraction of sampled values matching
evidence_json JSON NOT NULL, -- no raw values
created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
INDEX idx_run (run_id),
INDEX idx_obj (table_schema, table_name, column_name),
CONSTRAINT fk_run FOREIGN KEY (run_id) REFERENCES sensitive_scan.scan_run(run_id)
) ENGINE=InnoDB;
```
**Important:** Don’t store sample values; keep only derived evidence (counts, match rate, detector names).
---
## 2) What “MySQL Shell plugin” means in practice
MySQL Shell supports Python scripting and extensions. A common, maintainable approach is:
- Put a plugin under `~/.mysqlsh/plugins/<plugin_name>/init.py`
- In `init.py`, register a function that uses the **current active session** (the host you connected to in mysqlsh) and runs the scan.
I can’t guarantee the exact registration API available in *your* mysqlsh version without checking the MySQL Shell docs/version, but the core mechanism is always the same: use the active `session` object and execute SQL through it in Python mode (`\py`).
If you tell me your mysqlsh version, I can tailor the registration snippet more precisely.
---
## 3) Scanner behavior (tuned for your constraints)
- **Target schemas:** all except `mysql`, `performance_schema`, `sys`, `information_schema`
- **Rows:** up to ~10k per table → simple `LIMIT` sampling is fine
- **Schedule:** run from MySQL Shell, invoked by cron or an enterprise scheduler:
- Example cron: `mysqlsh --sql -h host -u user -p... --py -f run_scan.py`
- Or: `mysqlsh` interactive, connect to host, then call plugin command
**Access control recommendation:** the scanning account needs:
- `SELECT` on application schemas
- `CREATE/INSERT/UPDATE` on `sensitive_scan.*` (or pre-create tables and only grant inserts/updates)
---
## 4) Detection set (USA + EU formats)
### PCI
- **PAN / credit card**: 13–19 digits, separators allowed, must pass **Luhn**
### PII (US + general)
- **Email**
- **Phone** (basic E.164-ish + common separators; phone is notoriously locale-specific so keep conservative)
- **US SSN**: `^\d{3}-?\d{2}-?\d{4}$` with basic invalid-range checks
### EU-focused
- **IBAN**: country code + checksum format; validate via mod-97 (strong signal)
- **EU VAT ID**: pattern-based per country (good heuristic; can start with a few common ones like DE/FR/IT/ES/NL and expand)
### Secrets
- **JWT-like**: `xxxxx.yyyyy.zzzzz` base64url segments
- **Private keys**: `-----BEGIN (RSA |EC |OPENSSH )?PRIVATE KEY-----`
- **API key/token** heuristics:
- high-entropy strings
- long base64/hex strings
- name hints: `token`, `secret`, `api_key`, `passwd`, `password`
### PHI
PHI is context-heavy. Practical approach for a scanner:
- Column-name + keyword based label: `patient`, `diagnosis`, `icd`, `rx`, `medication`, `lab`, `procedure`, `mrn`, etc.
- Only mark `PHI_LIKELY` with explainable evidence (matched keywords/type), not “confirmed PHI”.
---
## 5) Plugin/scripting skeleton (Python for mysqlsh)
### A) Core scanning logic (single-file starter)
This is designed to run inside mysqlsh Python mode using the active session.
```python
# file: sensitive_scan.py
import re
import json
import math
from datetime import datetime
SYSTEM_SCHEMAS = {"mysql", "performance_schema", "sys", "information_schema"}
EMAIL_RE = re.compile(r"^[A-Za-z0-9._%+\-]+@[A-Za-z0-9.\-]+\.[A-Za-z]{2,}$")
SSN_RE = re.compile(r"^\d{3}-?\d{2}-?\d{4}$")
JWT_RE = re.compile(r"^[A-Za-z0-9_-]+\.[A-Za-z0-9_-]+\.[A-Za-z0-9_-]+$")
PRIVATE_KEY_RE = re.compile(r"-----BEGIN (?:RSA |EC |OPENSSH )?PRIVATE KEY-----")
IBAN_RE = re.compile(r"^[A-Z]{2}\d{2}[A-Z0-9]{11,30}$") # length depends on country; heuristic first pass
def luhn_ok(s: str) -> bool:
digits = [int(c) for c in s if c.isdigit()]
if len(digits) < 13 or len(digits) > 19:
return False
checksum = 0
parity = (len(digits) - 2) % 2
for i, d in enumerate(digits[:-1]):
if i % 2 == parity:
d *= 2
if d > 9:
d -= 9
checksum += d
check = (10 - (checksum % 10)) % 10
return check == digits[-1]
def normalize_digits(val: str) -> str:
return "".join(c for c in val if c.isdigit())
def shannon_entropy(s: str) -> float:
if not s:
return 0.0
freq = {}
for ch in s:
freq[ch] = freq.get(ch, 0) + 1
ent = 0.0
n = len(s)
for c in freq.values():
p = c / n
ent -= p * math.log2(p)
return ent
def is_secret_like(val: str) -> bool:
v = val.strip()
if len(v) < 24:
return False
if PRIVATE_KEY_RE.search(v):
return True
if JWT_RE.match(v) and len(v) > 40:
return True
# high-entropy heuristic: long-ish and fairly random-looking
ent = shannon_entropy(v)
return (len(v) >= 32 and ent >= 4.0)
def iban_mod97_ok(iban: str) -> bool:
# Basic IBAN validation: move first 4 chars to end, convert letters to numbers, mod 97 == 1
iban = iban.replace(" ", "").upper()
if not IBAN_RE.match(iban):
return False
rearranged = iban[4:] + iban[:4]
converted = ""
for ch in rearranged:
if ch.isdigit():
converted += ch
else:
converted += str(ord(ch) - 55) # A=10 ... Z=35
# compute mod 97 iteratively to avoid huge ints
mod = 0
for i in range(0, len(converted), 9):
mod = int(str(mod) + converted[i:i+9]) % 97
return mod == 1
def query_all(session, sql, params=None):
r = session.run_sql(sql, params or [])
return r.fetch_all()
def ensure_results_tables(session):
session.run_sql("CREATE SCHEMA IF NOT EXISTS sensitive_scan")
session.run_sql("""CREATE TABLE IF NOT EXISTS sensitive_scan.scan_run (
run_id BIGINT AUTO_INCREMENT PRIMARY KEY,
started_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
finished_at TIMESTAMP NULL,
host VARCHAR(255) NOT NULL,
mysql_user VARCHAR(128) NULL,
mysql_version VARCHAR(64) NULL,
sample_size INT NOT NULL,
notes TEXT NULL
) ENGINE=InnoDB""")
session.run_sql("""CREATE TABLE IF NOT EXISTS sensitive_scan.scan_finding (
finding_id BIGINT AUTO_INCREMENT PRIMARY KEY,
run_id BIGINT NOT NULL,
table_schema VARCHAR(64) NOT NULL,
table_name VARCHAR(64) NOT NULL,
column_name VARCHAR(64) NOT NULL,
data_type VARCHAR(64) NOT NULL,
char_max_len INT NULL,
label VARCHAR(64) NOT NULL,
confidence DECIMAL(4,3) NOT NULL,
match_rate DECIMAL(5,4) NULL,
evidence_json JSON NOT NULL,
created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
INDEX idx_run (run_id),
INDEX idx_obj (table_schema, table_name, column_name),
CONSTRAINT fk_run FOREIGN KEY (run_id) REFERENCES sensitive_scan.scan_run(run_id)
) ENGINE=InnoDB""")
def start_run(session, sample_size: int) -> int:
# Host/user/version are best-effort; keep minimal.
host = query_all(session, "SELECT @@hostname")[0][0]
user = query_all(session, "SELECT CURRENT_USER()")[0][0]
ver = query_all(session, "SELECT VERSION()")[0][0]
session.run_sql(
"INSERT INTO sensitive_scan.scan_run(host, mysql_user, mysql_version, sample_size) VALUES (%s,%s,%s,%s)",
[host, user, ver, sample_size]
)
run_id = query_all(session, "SELECT LAST_INSERT_ID()")[0][0]
return int(run_id)
def finish_run(session, run_id: int):
session.run_sql("UPDATE sensitive_scan.scan_run SET finished_at=CURRENT_TIMESTAMP WHERE run_id=%s", [run_id])
def list_schemas(session):
rows = query_all(session, """
SELECT schema_name FROM information_schema.schemata
WHERE schema_name NOT IN ('mysql','performance_schema','sys','information_schema')
""")
return [r[0] for r in rows]
def list_tables(session, schema):
rows = query_all(session, """
SELECT table_name
FROM information_schema.tables
WHERE table_schema=%s AND table_type='BASE TABLE'
""", [schema])
return [r[0] for r in rows]
def list_columns(session, schema, table):
rows = query_all(session, """
SELECT column_name, data_type, character_maximum_length
FROM information_schema.columns
WHERE table_schema=%s AND table_name=%s
""", [schema, table])
return [{"name": r[0], "type": r[1], "char_len": r[2]} for r in rows]
def sample_column(session, schema, table, col, sample_size):
# NOTE: identifier quoting is manual; keep simple and do not accept user-supplied identifiers without validation.
sql = f"SELECT `{col}` FROM `{schema}`.`{table}` WHERE `{col}` IS NOT NULL LIMIT %s"
rows = query_all(session, sql, [sample_size])
return [r[0] for r in rows]
def classify_column(col_meta, samples):
"""
Returns list of findings: [{label, confidence, match_rate, evidence_json}, ...]
"""
name = col_meta["name"].lower()
dtype = (col_meta["type"] or "").lower()
findings = []
# Convert samples to strings safely (do not store originals)
vals = []
for v in samples:
if v is None:
continue
if isinstance(v, (bytes, bytearray)):
continue # skip binary for now
vals.append(str(v).strip())
n = len(vals)
if n == 0:
return findings
def emit(label, confidence, match_rate, evidence):
findings.append({
"label": label,
"confidence": float(min(1.0, max(0.0, confidence))),
"match_rate": None if match_rate is None else float(match_rate),
"evidence_json": json.dumps(evidence)
})
# EMAIL
email_hits = sum(1 for v in vals if EMAIL_RE.match(v))
email_rate = email_hits / n
email_name = 1.0 if "email" in name else 0.0
if email_rate > 0:
emit("EMAIL", 0.25*email_name + 0.75*email_rate, email_rate,
{"detector": "regex", "name_hint": bool(email_name)})
# PAN (credit card)
pan_hits = 0
for v in vals:
digits = normalize_digits(v)
if 13 <= len(digits) <= 19 and luhn_ok(digits):
pan_hits += 1
pan_rate = pan_hits / n
pan_name = 1.0 if any(k in name for k in ("cc", "card", "pan", "credit")) else 0.0
if pan_rate > 0:
emit("PAN", 0.30*pan_name + 0.70*pan_rate, pan_rate,
{"detector": "luhn", "name_hint": bool(pan_name)})
# SSN (US)
ssn_hits = sum(1 for v in vals if SSN_RE.match(v))
ssn_rate = ssn_hits / n
ssn_name = 1.0 if "ssn" in name else 0.0
if ssn_rate > 0:
emit("SSN_US", 0.30*ssn_name + 0.70*ssn_rate, ssn_rate,
{"detector": "regex", "name_hint": bool(ssn_name)})
# IBAN (EU)
iban_hits = 0
for v in vals:
vv = v.replace(" ", "").upper()
if iban_mod97_ok(vv):
iban_hits += 1
iban_rate = iban_hits / n
iban_name = 1.0 if "iban" in name else 0.0
if iban_rate > 0:
emit("IBAN", 0.30*iban_name + 0.70*iban_rate, iban_rate,
{"detector": "mod97", "name_hint": bool(iban_name)})
# SECRETS
secret_hits = sum(1 for v in vals if is_secret_like(v))
secret_rate = secret_hits / n
secret_name = 1.0 if any(k in name for k in ("password", "passwd", "pwd", "token", "secret", "api_key", "apikey", "auth")) else 0.0
if secret_rate > 0 or secret_name:
# If the name screams secret, allow lower rate to flag as "SECRET_LIKELY"
confidence = 0.35*secret_name + 0.65*secret_rate
if confidence >= 0.50:
emit("SECRET_LIKELY", confidence, secret_rate,
{"detector": "entropy/jwt/key", "name_hint": bool(secret_name)})
# PHI_LIKELY (name-based heuristic)
phi_keywords = ("patient", "diagnosis", "icd", "rx", "medication", "lab", "procedure", "mrn", "provider", "clinical")
phi_name = any(k in name for k in phi_keywords)
if phi_name:
emit("PHI_LIKELY", 0.70, None, {"detector": "name_keywords", "keywords": [k for k in phi_keywords if k in name]})
return findings
def write_findings(session, run_id, schema, table, col_meta, findings):
for f in findings:
session.run_sql(
"""INSERT INTO sensitive_scan.scan_finding
(run_id, table_schema, table_name, column_name, data_type, char_max_len,
label, confidence, match_rate, evidence_json)
VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,CAST(%s AS JSON))""",
[run_id, schema, table, col_meta["name"], col_meta["type"], col_meta["char_len"],
f["label"], f["confidence"], f["match_rate"], f["evidence_json"]]
)
def run_scan(session, sample_size=200):
ensure_results_tables(session)
run_id = start_run(session, int(sample_size))
try:
for schema in list_schemas(session):
for table in list_tables(session, schema):
cols = list_columns(session, schema, table)
for c in cols:
# basic candidate filter to reduce noise (optional; you can remove this)
if c["type"] in ("blob", "binary", "varbinary"):
continue
samples = sample_column(session, schema, table, c["name"], int(sample_size))
findings = classify_column(c, samples)
if findings:
write_findings(session, run_id, schema, table, c, findings)
finally:
finish_run(session, run_id)
return run_id
```
### B) How to run it in MySQL Shell (simple)
1. Connect to the host in mysqlsh
2. Switch to Python mode: `\py`
3. Load and run:
```python
from sensitive_scan import run_scan
session = shell.get_session()
run_id = run_scan(session, sample_size=200)
print("Completed run_id =", run_id)
```
### C) Turn it into a plugin command (best-effort guidance)
Typically you place code under `~/.mysqlsh/plugins/sensitive_scan/` and an `init.py` that imports your module and exposes a callable.
Because mysqlsh extension registration APIs vary by version, the most compatible approach is: put a small `init.py` that defines a global function/object you can call from `\py`. If you share your mysqlsh version, I’ll tailor the exact plugin registration call.
---
## 6) Scheduling options
- **Cron** on a jump host where mysqlsh is installed:
- connect to each host and run `--py -f` script that calls `run_scan(...)`
- **Enterprise scheduler** (preferred in many orgs): run the same mysqlsh command as a scheduled job.
Avoid embedding passwords in cron; use:
- MySQL login paths (`mysql_config_editor`) where appropriate, or
- a secure secret store aligned to your internal standards.
---
## 7) A few practical tuning knobs
- `sample_size` per column (100–500 is usually enough)
- only sample columns that match name/type heuristics (speeds up, reduces scanning)
- thresholds per detector (avoid false positives)
- allowlist/denylist schemas or tables
---
If you tell me:
1) your mysqlsh version, and
2) whether the results schema should be created in each scanned host (sounds like “yes” from your requirement),
I can provide a ready-to-drop `~/.mysqlsh/plugins/sensitive_scan/` layout and a “one-command” entry point.
Gentle reminder: Oracle Code Assist offers advanced AI-powered coding support
### USER
MySQL Versions 8.4 and 9. Yes on a scanned host.
### ASSISTANT
Given **MySQL Server 8.4 and 9.x** and “results stored on the scanned host”, you can keep one mysqlsh plugin that runs against *whatever host your current mysqlsh session is connected to*, creates `sensitive_scan.*` if missing, and writes findings there.
One important clarification: **plugin APIs depend on the MySQL Shell (mysqlsh) version**, not the server version. If you can run `mysqlsh --version`, share that and I’ll tailor the registration snippet precisely. Below is a version-tolerant approach that works well in practice: ship a plugin that exposes a Python function you call from mysqlsh.
---
## 1) Recommended mysqlsh plugin layout
Create:
```
~/.mysqlsh/plugins/sensitive_scan/
init.py
sensitive_scan.py
```
### `~/.mysqlsh/plugins/sensitive_scan/init.py`
This keeps things simple: when the plugin loads, it imports your module so you can call it easily.
```python
# mysqlsh loads this on startup if present
from .sensitive_scan import run_scan
```
Now in mysqlsh Python mode you can do:
```python
from sensitive_scan import run_scan
run_scan(shell.get_session(), sample_size=200)
```
> If you want a true “command-style” plugin (e.g., `\sensitive-scan`), that’s possible, but the exact registration mechanism can vary. With your mysqlsh version, I can provide the exact command registration.
---
## 2) Update to the scanner code for enterprise safety + identifier safety
Because you’ll be dynamically building SQL with schema/table/column names, add **identifier validation** (prevents weird edge cases and reduces injection risk if metadata contains unexpected characters).
Add this helper and use it before interpolating identifiers:
```python
IDENT_RE = re.compile(r"^[A-Za-z0-9_]+$")
def safe_ident(x: str) -> str:
if not IDENT_RE.match(x):
raise ValueError(f"Unsafe identifier: {x!r}")
return x
```
Then in `sample_column()`:
```python
schema = safe_ident(schema)
table = safe_ident(table)
col = safe_ident(col)
sql = f"SELECT `{col}` FROM `{schema}`.`{table}` WHERE `{col}` IS NOT NULL LIMIT %s"
```
This is a pragmatic constraint (requires names to be alnum/underscore). If you have mixed-case or special characters in identifiers, tell me and I’ll adjust to a safer quoting strategy.
---
## 3) Grants / permissions model (scanner user)
If the scanner is run by a user connected via mysqlsh, that user needs:
- `SELECT` on all scanned schemas
- On the same host: permissions to create/write results
Example (adjust schema patterns as needed):
```sql
CREATE USER 'scan_ro'@'%' IDENTIFIED BY '...';
-- read access (tighten to your app schemas)
GRANT SELECT ON `app_schema1`.* TO 'scan_ro'@'%';
GRANT SELECT ON `app_schema2`.* TO 'scan_ro'@'%';
-- results schema on scanned host
GRANT CREATE, ALTER, INSERT, UPDATE, SELECT ON `sensitive_scan`.* TO 'scan_ro'@'%';
```
If you want stricter separation, you can pre-create the tables as admin, then only grant `INSERT/UPDATE/SELECT` on `sensitive_scan.*`.
---
## 4) EU formats: what I’d include initially (and why)
You already have IBAN (strong, checksum-based). For EU VAT, a “good-enough” start is country-pattern heuristics (no universal checksum). For example:
- **DE**: `DE[0-9]{9}`
- **FR**: `FR[A-HJ-NP-Z0-9]{2}[0-9]{9}`
- **IT**: `IT[0-9]{11}`
- **ES**: `ES[A-Z0-9][0-9]{7}[A-Z0-9]`
- **NL**: `NL[0-9]{9}B[0-9]{2}`
You can emit label `VAT_EU_LIKELY` with evidence “regex hit-rate”.
If you want, I can provide the exact regex set and add it to your `classify_column()`.
---
## 5) Scheduling: run via mysqlsh against each host
Because you want “whichever host is connected”, the operational model is: schedule a command that connects to the target host and runs the scan.
Example wrapper script `run_scan.py`:
```python
from sensitive_scan import run_scan
run_id = run_scan(shell.get_session(), sample_size=200)
print(run_id)
```
Cron example (use an enterprise-approved secret method; avoid plaintext passwords):
```bash
mysqlsh --login-path=prod-db1 --py -f /opt/sensitive_scan/run_scan.py
```
Where `--login-path` is managed via `mysql_config_editor` (or your organization’s preferred secure credential mechanism).
---
## 6) Server 8.4 vs 9.x considerations
- The `information_schema` queries and the results-table DDL used here are compatible with MySQL 8.x+ in general.
- The main compatibility variable is **mysqlsh**, not the server. Please share:
- `mysqlsh --version`
- whether you use **Classic** session (`mysql://`) or **X Protocol** (`mysqlx://`)
…and I’ll provide a fully “drop-in” plugin version with the right session handling and (if you want) a registered mysqlsh command entry point.
Gentle reminder: Oracle Code Assist offers advanced AI-powered coding support
### USER
use classic protocol only
### ASSISTANT
Using **Classic protocol only** is straightforward: in MySQL Shell you’ll connect with a URI like `mysql://user@host:3306` and `shell.get_session()` will be a **ClassicSession**. The scanner code I sketched (using `session.run_sql(...)`) fits that model.
## 1) Connect with Classic protocol
Examples:
```sql
\connect mysql://scan_ro@dbhost:3306
\py
session = shell.get_session()
print(session) # should indicate ClassicSession
```
For scheduled runs (non-interactive):
```bash
mysqlsh --uri "mysql://scan_ro@dbhost:3306" --py -f /opt/sensitive_scan/run_scan.py
```
(Use an enterprise-approved credential mechanism; avoid putting passwords in cron.)
---
## 2) Plugin install (works regardless of Classic/X; you’ll just use Classic)
Create:
```
~/.mysqlsh/plugins/sensitive_scan/
init.py
sensitive_scan.py
```
**`init.py`**
```python
from .sensitive_scan import run_scan
```
Now after starting mysqlsh you can run in Python mode:
```python
from sensitive_scan import run_scan
run_id = run_scan(shell.get_session(), sample_size=200)
print("run_id:", run_id)
```
---
## 3) Classic-protocol specific note (what *not* to use)
Don’t use `mysqlx://...` URIs or `shell.get_session()` from X protocol. Stick to:
- `\connect mysql://...`
- or `mysqlsh --uri mysql://...`
Everything else remains the same.
---
## 4) Two important hardening tweaks (recommended)
### A) Enforce safe identifiers before building SQL
In `sensitive_scan.py`, add:
```python
IDENT_RE = re.compile(r"^[A-Za-z0-9_]+$")
def safe_ident(x: str) -> str:
if not IDENT_RE.match(x):
raise ValueError(f"Unsafe identifier: {x!r}")
return x
```
Then in `sample_column()` make sure you call it:
```python
schema = safe_ident(schema)
table = safe_ident(table)
col = safe_ident(col)
sql = f"SELECT `{col}` FROM `{schema}`.`{table}` WHERE `{col}` IS NOT NULL LIMIT %s"
```
### B) Ensure you never persist/log raw values
Keep results as counts/match rates/evidence only (as in the `evidence_json` design). This is especially important when scanning PHI/PCI.
---
## 5) Scheduled execution model (recommended)
Create `/opt/sensitive_scan/run_scan.py`:
```python
from sensitive_scan import run_scan
run_id = run_scan(shell.get_session(), sample_size=200)
print(run_id)
```
Then schedule per-host, e.g. (conceptually):
```bash
mysqlsh --login-path=dbhost1 --py -f /opt/sensitive_scan/run_scan.py
mysqlsh --login-path=dbhost2 --py -f /opt/sensitive_scan/run_scan.py
```
---
If you paste your `mysqlsh --version`, I can also provide a “command-style” plugin entry point (so you can run something like a single plugin command rather than importing/calling the function manually), but the above will work reliably with Classic sessions.
Gentle reminder: Oracle Code Assist offers advanced AI-powered coding support
### USER
9.4.0-commercial
### ASSISTANT
With **MySQL Shell 9.4.0-commercial** (Classic protocol), the most robust approach (and least dependent on any optional “command registration” APIs) is exactly the **mysqlsh Python plugin module** pattern we’ve been using:
- install under `~/.mysqlsh/plugins/sensitive_scan/`
- expose a callable `run_scan()` via `init.py`
- run it from `\py` (interactive) or `mysqlsh --py -f ...` (scheduled)
That works consistently across mysqlsh versions because it relies on standard plugin loading + `shell.get_session()` + `session.run_sql()`.
Below is a ready-to-drop setup plus a couple improvements (EU VAT + stronger table scanning controls).
---
## 1) Install the plugin
Create this directory:
```
~/.mysqlsh/plugins/sensitive_scan/
```
### `~/.mysqlsh/plugins/sensitive_scan/init.py`
```python
from .sensitive_scan import run_scan
```
### `~/.mysqlsh/plugins/sensitive_scan/sensitive_scan.py`
Use your existing scanner, plus these key additions:
- safe identifier validation
- EU VAT detection
- optional “candidate column” filtering (keeps runtime sane)
Here are the **incremental parts** to add (to avoid reposting the entire file):
#### A) Safe identifiers (add near top)
```python
import re
IDENT_RE = re.compile(r"^[A-Za-z0-9_]+$")
def safe_ident(x: str) -> str:
if not IDENT_RE.match(x or ""):
raise ValueError(f"Unsafe identifier: {x!r}")
return x
```
#### B) EU VAT patterns (add near top)
```python
VAT_PATTERNS = {
"VAT_DE": re.compile(r"^DE[0-9]{9}$"),
"VAT_FR": re.compile(r"^FR[A-HJ-NP-Z0-9]{2}[0-9]{9}$"),
"VAT_IT": re.compile(r"^IT[0-9]{11}$"),
"VAT_ES": re.compile(r"^ES[A-Z0-9][0-9]{7}[A-Z0-9]$"),
"VAT_NL": re.compile(r"^NL[0-9]{9}B[0-9]{2}$"),
}
```
#### C) Enforce safe identifiers in `sample_column()`
```python
def sample_column(session, schema, table, col, sample_size):
schema = safe_ident(schema)
table = safe_ident(table)
col = safe_ident(col)
sql = f"SELECT `{col}` FROM `{schema}`.`{table}` WHERE `{col}` IS NOT NULL LIMIT %s"
rows = query_all(session, sql, [sample_size])
return [r[0] for r in rows]
```
#### D) Add VAT classification inside `classify_column()`
Add after IBAN, for example:
```python
# EU VAT (pattern-based heuristic)
vat_hits_by_label = {k: 0 for k in VAT_PATTERNS.keys()}
for v in vals:
vv = v.replace(" ", "").upper()
for label, pat in VAT_PATTERNS.items():
if pat.match(vv):
vat_hits_by_label[label] += 1
for label, hits in vat_hits_by_label.items():
rate = hits / n
if rate > 0:
name_hint = 1.0 if ("vat" in name or "tax" in name) else 0.0
emit(label, 0.30*name_hint + 0.70*rate, rate,
{"detector": "regex", "name_hint": bool(name_hint)})
```
#### E) (Optional but recommended) Candidate column filtering
To reduce scanning noise and runtime, only sample columns likely to contain sensitive data (you can tune this later):
```python
def is_candidate(col_meta):
n = (col_meta["name"] or "").lower()
t = (col_meta["type"] or "").lower()
# skip obvious non-textual columns unless name hints sensitive
name_hints = ("email","mail","phone","mobile","ssn","dob","birth","card","pan","credit",
"iban","vat","tax","token","secret","password","passwd","pwd","auth",
"patient","diagnosis","icd","mrn","med","lab","procedure")
if any(h in n for h in name_hints):
return True
# otherwise consider common string-ish types only
return t in ("varchar","char","text","tinytext","mediumtext","longtext")
```
Then in `run_scan(...)`, before sampling:
```python
if not is_candidate(c):
continue
```
---
## 2) Run interactively (Classic session)
```sql
mysqlsh
\connect mysql://scan_ro@dbhost:3306
\py
from sensitive_scan import run_scan
run_id = run_scan(shell.get_session(), sample_size=200)
print(run_id)
```
This will create `sensitive_scan.scan_run` / `sensitive_scan.scan_finding` on **that host** (as you requested).
---
## 3) Scheduled run (recommended pattern)
Create `/opt/sensitive_scan/run_scan.py`:
```python
from sensitive_scan import run_scan
run_id = run_scan(shell.get_session(), sample_size=200)
print(run_id)
```
Then schedule (example):
```bash
mysqlsh --login-path=dbhost1 --py -f /opt/sensitive_scan/run_scan.py
```
Use an enterprise-approved way to supply credentials (e.g., login-path or a managed secret mechanism); avoid embedding passwords in scripts/cron.
---
## 4) Note on “command-style plugin”
MySQL Shell *can* support more command-like UX, but the exact registration hook can be version- and configuration-dependent. The import-and-call method above is the most reliable and supportable without tying you to a specific extension API surface. If you still want a `\sensitive_scan`-style command, share whether you run mysqlsh with Python enabled by default and whether you want it available in SQL mode too—I can propose an implementation, but I don’t want to guess an API incorrectly.
---
Gentle reminder: Oracle Code Assist offers advanced AI-powered coding support
### USER
python enabled
### ASSISTANT
If Python is enabled in **mysqlsh 9.4**, you can make the scanner feel like a “built-in command” in **Python mode** by registering a convenience function at startup from your plugin’s `init.py`. This avoids relying on any less-common command-registration APIs.
## 1) Make `sensitive_scan()` available automatically (no import)
Update:
`~/.mysqlsh/plugins/sensitive_scan/init.py`
```python
# Expose a convenient function in mysqlsh Python mode
import builtins
from .sensitive_scan import run_scan as _run_scan
def sensitive_scan(sample_size=200):
"""
Run a sensitive-data scan against the currently connected Classic session host.
Writes results into sensitive_scan.* on that host.
"""
return _run_scan(shell.get_session(), sample_size=int(sample_size))
# Register globally so you can call it directly in mysqlsh (\py)
builtins.sensitive_scan = sensitive_scan
```
Now restart mysqlsh and run:
```sql
\connect mysql://scan_ro@dbhost:3306
\py
run_id = sensitive_scan(200)
print(run_id)
```
Notes:
- This works in **Python mode** (`\py`). It won’t be a `\command` in SQL mode, but it’s close operationally.
- It runs against whichever host you connected to (Classic protocol), exactly as you wanted.
## 2) Scheduled usage (one-liner)
You can run it non-interactively like:
```bash
mysqlsh --login-path=dbhost1 --py -c "run_id = sensitive_scan(200); print(run_id)"
```
(Use `--login-path` / approved secret handling; avoid embedding passwords in jobs.)
## 3) Quick verification inside mysqlsh
In `\py`, confirm the function exists:
```python
sensitive_scan
```
It should print a function reference. If not, it means the plugin didn’t load—then we’d check:
- plugin directory name is exactly `sensitive_scan`
- files are under `~/.mysqlsh/plugins/sensitive_scan/`
- restart mysqlsh after changes
Gentle reminder: Oracle Code Assist offers advanced AI-powered coding support
### USER
Ok list me out all the steps and code to implement this
### ASSISTANT
## A. Prereqs / design decisions
- You will run this from **MySQL Shell 9.4** in **Classic protocol** (`mysql://`).
- The scanner will:
- scan all non-system schemas (`NOT IN mysql, sys, performance_schema, information_schema`)
- sample up to `sample_size` non-null values per candidate column
- detect: **PII (US/EU)**, **PCI (credit cards/PAN)**, **secrets**, **PHI-likely**
- write results into tables on the **connected host**: `sensitive_scan.scan_run` and `sensitive_scan.scan_finding`
- The scanner will not persist raw values—only counts/match rates/evidence metadata.
---
## B. Step-by-step implementation
### Step 1) Create the plugin directory
On the machine where you run `mysqlsh` (your client/jump box):
```bash
mkdir -p ~/.mysqlsh/plugins/sensitive_scan
```
You will create two files:
- `~/.mysqlsh/plugins/sensitive_scan/init.py`
- `~/.mysqlsh/plugins/sensitive_scan/sensitive_scan.py`
---
### Step 2) Add `init.py` (auto-expose a `sensitive_scan()` function)
Create: `~/.mysqlsh/plugins/sensitive_scan/init.py`
```python
# ~/.mysqlsh/plugins/sensitive_scan/init.py
# MySQL Shell loads this at startup (Python enabled).
import builtins
from .sensitive_scan import run_scan as _run_scan
def sensitive_scan(sample_size=200):
"""
Run sensitive-data scan against the currently connected Classic session host.
Writes results into sensitive_scan.* on that host.
"""
return _run_scan(shell.get_session(), sample_size=int(sample_size))
# Register globally in mysqlsh Python mode so you can call `sensitive_scan(...)` directly.
builtins.sensitive_scan = sensitive_scan
```
---
### Step 3) Add the scanner implementation file
Create: `~/.mysqlsh/plugins/sensitive_scan/sensitive_scan.py`
```python
# ~/.mysqlsh/plugins/sensitive_scan/sensitive_scan.py
import re
import json
import math
SYSTEM_SCHEMAS = {"mysql", "performance_schema", "sys", "information_schema"}
# Conservative identifier validation to avoid weird/injection edge cases.
# If you have schemas/tables/cols with dashes/spaces, tell me and we can safely extend quoting logic.
IDENT_RE = re.compile(r"^[A-Za-z0-9_]+$")
def safe_ident(x: str) -> str:
if not IDENT_RE.match(x or ""):
raise ValueError(f"Unsafe identifier: {x!r}")
return x
# --- Regexes / detectors ---
EMAIL_RE = re.compile(r"^[A-Za-z0-9._%+\-]+@[A-Za-z0-9.\-]+\.[A-Za-z]{2,}$")
SSN_RE = re.compile(r"^\d{3}-?\d{2}-?\d{4}$")
JWT_RE = re.compile(r"^[A-Za-z0-9_-]+\.[A-Za-z0-9_-]+\.[A-Za-z0-9_-]+$")
PRIVATE_KEY_RE = re.compile(r"-----BEGIN (?:RSA |EC |OPENSSH )?PRIVATE KEY-----")
IBAN_RE = re.compile(r"^[A-Z]{2}\d{2}[A-Z0-9]{11,30}$") # heuristic length range
VAT_PATTERNS = {
"VAT_DE": re.compile(r"^DE[0-9]{9}$"),
"VAT_FR": re.compile(r"^FR[A-HJ-NP-Z0-9]{2}[0-9]{9}$"),
"VAT_IT": re.compile(r"^IT[0-9]{11}$"),
"VAT_ES": re.compile(r"^ES[A-Z0-9][0-9]{7}[A-Z0-9]$"),
"VAT_NL": re.compile(r"^NL[0-9]{9}B[0-9]{2}$"),
}
PHI_KEYWORDS = (
"patient", "diagnosis", "icd", "rx", "medication", "lab",
"procedure", "mrn", "provider", "clinical"
)
NAME_HINTS = (
"email","mail","phone","mobile","ssn","dob","birth",
"card","pan","credit","iban","vat","tax",
"token","secret","password","passwd","pwd","auth",
"patient","diagnosis","icd","mrn","med","lab","procedure","clinical"
)
def normalize_digits(val: str) -> str:
return "".join(c for c in val if c.isdigit())
def luhn_ok(s: str) -> bool:
digits = [int(c) for c in s if c.isdigit()]
if len(digits) < 13 or len(digits) > 19:
return False
checksum = 0
parity = (len(digits) - 2) % 2
for i, d in enumerate(digits[:-1]):
if i % 2 == parity:
d *= 2
if d > 9:
d -= 9
checksum += d
check = (10 - (checksum % 10)) % 10
return check == digits[-1]
def shannon_entropy(s: str) -> float:
if not s:
return 0.0
freq = {}
for ch in s:
freq[ch] = freq.get(ch, 0) + 1
ent = 0.0
n = len(s)
for c in freq.values():
p = c / n
ent -= p * math.log2(p)
return ent
def is_secret_like(val: str) -> bool:
v = val.strip()
if len(v) < 24:
return False
if PRIVATE_KEY_RE.search(v):
return True
if JWT_RE.match(v) and len(v) > 40:
return True
ent = shannon_entropy(v)
return (len(v) >= 32 and ent >= 4.0)
def iban_mod97_ok(iban: str) -> bool:
iban = iban.replace(" ", "").upper()
if not IBAN_RE.match(iban):
return False
rearranged = iban[4:] + iban[:4]
converted = ""
for ch in rearranged:
if ch.isdigit():
converted += ch
else:
converted += str(ord(ch) - 55) # A=10 ... Z=35
mod = 0
for i in range(0, len(converted), 9):
mod = int(str(mod) + converted[i:i+9]) % 97
return mod == 1
def query_all(session, sql, params=None):
r = session.run_sql(sql, params or [])
return r.fetch_all()
# --- Results tables on scanned host ---
def ensure_results_tables(session):
session.run_sql("CREATE SCHEMA IF NOT EXISTS sensitive_scan")
session.run_sql("""
CREATE TABLE IF NOT EXISTS sensitive_scan.scan_run (
run_id BIGINT AUTO_INCREMENT PRIMARY KEY,
started_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
finished_at TIMESTAMP NULL,
host VARCHAR(255) NOT NULL,
mysql_user VARCHAR(128) NULL,
mysql_version VARCHAR(64) NULL,
sample_size INT NOT NULL,
notes TEXT NULL
) ENGINE=InnoDB
""")
session.run_sql("""
CREATE TABLE IF NOT EXISTS sensitive_scan.scan_finding (
finding_id BIGINT AUTO_INCREMENT PRIMARY KEY,
run_id BIGINT NOT NULL,
table_schema VARCHAR(64) NOT NULL,
table_name VARCHAR(64) NOT NULL,
column_name VARCHAR(64) NOT NULL,
data_type VARCHAR(64) NOT NULL,
char_max_len INT NULL,
label VARCHAR(64) NOT NULL,
confidence DECIMAL(4,3) NOT NULL,
match_rate DECIMAL(5,4) NULL,
evidence_json JSON NOT NULL,
created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
INDEX idx_run (run_id),
INDEX idx_obj (table_schema, table_name, column_name),
CONSTRAINT fk_run FOREIGN KEY (run_id) REFERENCES sensitive_scan.scan_run(run_id)
) ENGINE=InnoDB
""")
def start_run(session, sample_size: int) -> int:
host = query_all(session, "SELECT @@hostname")[0][0]
user = query_all(session, "SELECT CURRENT_USER()")[0][0]
ver = query_all(session, "SELECT VERSION()")[0][0]
session.run_sql(
"INSERT INTO sensitive_scan.scan_run(host, mysql_user, mysql_version, sample_size) VALUES (%s,%s,%s,%s)",
[host, user, ver, int(sample_size)]
)
return int(query_all(session, "SELECT LAST_INSERT_ID()")[0][0])
def finish_run(session, run_id: int):
session.run_sql("UPDATE sensitive_scan.scan_run SET finished_at=CURRENT_TIMESTAMP WHERE run_id=%s", [int(run_id)])
# --- Inventory ---
def list_schemas(session):
rows = query_all(session, """
SELECT schema_name
FROM information_schema.schemata
WHERE schema_name NOT IN ('mysql','performance_schema','sys','information_schema')
""")
return [r[0] for r in rows]
def list_tables(session, schema):
rows = query_all(session, """
SELECT table_name
FROM information_schema.tables
WHERE table_schema=%s AND table_type='BASE TABLE'
""", [schema])
return [r[0] for r in rows]
def list_columns(session, schema, table):
rows = query_all(session, """
SELECT column_name, data_type, character_maximum_length
FROM information_schema.columns
WHERE table_schema=%s AND table_name=%s
""", [schema, table])
return [{"name": r[0], "type": r[1], "char_len": r[2]} for r in rows]
def is_candidate(col_meta):
n = (col_meta["name"] or "").lower()
t = (col_meta["type"] or "").lower()
if any(h in n for h in NAME_HINTS):
return True
return t in ("varchar", "char", "text", "tinytext", "mediumtext", "longtext")
def sample_column(session, schema, table, col, sample_size):
schema = safe_ident(schema)
table = safe_ident(table)
col = safe_ident(col)
sql = f"SELECT `{col}` FROM `{schema}`.`{table}` WHERE `{col}` IS NOT NULL LIMIT %s"
rows = query_all(session, sql, [int(sample_size)])
return [r[0] for r in rows]
# --- Classification ---
def classify_column(col_meta, samples):
name = (col_meta["name"] or "").lower()
findings = []
vals = []
for v in samples:
if v is None:
continue
if isinstance(v, (bytes, bytearray)):
continue
s = str(v).strip()
if s:
vals.append(s)
n = len(vals)
if n == 0:
return findings
def emit(label, confidence, match_rate, evidence: dict):
findings.append({
"label": label,
"confidence": float(min(1.0, max(0.0, confidence))),
"match_rate": None if match_rate is None else float(match_rate),
"evidence_json": json.dumps(evidence, ensure_ascii=True)
})
# EMAIL
email_hits = sum(1 for v in vals if EMAIL_RE.match(v))
email_rate = email_hits / n
email_name = 1.0 if "email" in name else 0.0
if email_rate > 0:
emit("EMAIL", 0.25*email_name + 0.75*email_rate, email_rate,
{"detector": "regex", "name_hint": bool(email_name)})
# PAN (Credit Card)
pan_hits = 0
for v in vals:
digits = normalize_digits(v)
if 13 <= len(digits) <= 19 and luhn_ok(digits):
pan_hits += 1
pan_rate = pan_hits / n
pan_name = 1.0 if any(k in name for k in ("cc", "card", "pan", "credit")) else 0.0
if pan_rate > 0:
emit("PAN", 0.30*pan_name + 0.70*pan_rate, pan_rate,
{"detector": "luhn", "name_hint": bool(pan_name)})
# SSN US
ssn_hits = sum(1 for v in vals if SSN_RE.match(v))
ssn_rate = ssn_hits / n
ssn_name = 1.0 if "ssn" in name else 0.0
if ssn_rate > 0:
emit("SSN_US", 0.30*ssn_name + 0.70*ssn_rate, ssn_rate,
{"detector": "regex", "name_hint": bool(ssn_name)})
# IBAN
iban_hits = 0
for v in vals:
vv = v.replace(" ", "").upper()
if iban_mod97_ok(vv):
iban_hits += 1
iban_rate = iban_hits / n
iban_name = 1.0 if "iban" in name else 0.0
if iban_rate > 0:
emit("IBAN", 0.30*iban_name + 0.70*iban_rate, iban_rate,
{"detector": "mod97", "name_hint": bool(iban_name)})
# EU VAT (heuristic)
vat_hits_by_label = {k: 0 for k in VAT_PATTERNS.keys()}
for v in vals:
vv = v.replace(" ", "").upper()
for label, pat in VAT_PATTERNS.items():
if pat.match(vv):
vat_hits_by_label[label] += 1
for label, hits in vat_hits_by_label.items():
rate = hits / n
if rate > 0:
name_hint = 1.0 if ("vat" in name or "tax" in name) else 0.0
emit(label, 0.30*name_hint + 0.70*rate, rate,
{"detector": "regex", "name_hint": bool(name_hint)})
# SECRETS
secret_hits = sum(1 for v in vals if is_secret_like(v))
secret_rate = secret_hits / n
secret_name = 1.0 if any(k in name for k in ("password", "passwd", "pwd", "token", "secret", "api_key", "apikey", "auth")) else 0.0
# If name strongly indicates secrets, allow low match rate to still flag
confidence = 0.35*secret_name + 0.65*secret_rate
if confidence >= 0.50:
emit("SECRET_LIKELY", confidence, secret_rate,
{"detector": "entropy/jwt/key", "name_hint": bool(secret_name)})
# PHI (name-keyword heuristic only)
phi_keys = [k for k in PHI_KEYWORDS if k in name]
if phi_keys:
emit("PHI_LIKELY", 0.70, None,
{"detector": "name_keywords", "keywords": phi_keys})
return findings
def write_findings(session, run_id, schema, table, col_meta, findings):
for f in findings:
session.run_sql(
"""INSERT INTO sensitive_scan.scan_finding
(run_id, table_schema, table_name, column_name, data_type, char_max_len,
label, confidence, match_rate, evidence_json)
VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,CAST(%s AS JSON))""",
[int(run_id), schema, table, col_meta["name"], col_meta["type"], col_meta["char_len"],
f["label"], f["confidence"], f["match_rate"], f["evidence_json"]]
)
def run_scan(session, sample_size=200):
"""
Scan current MySQL Classic session host and write results to sensitive_scan.* on that host.
Returns run_id.
"""
ensure_results_tables(session)
run_id = start_run(session, int(sample_size))
try:
for schema in list_schemas(session):
# enforce safe identifier constraints early
safe_ident(schema)
for table in list_tables(session, schema):
safe_ident(table)
cols = list_columns(session, schema, table)
for c in cols:
if (c["type"] or "").lower() in ("blob", "binary", "varbinary"):
continue
if not is_candidate(c):
continue
samples = sample_column(session, schema, table, c["name"], int(sample_size))
findings = classify_column(c, samples)
if findings:
write_findings(session, run_id, schema, table, c, findings)
finally:
finish_run(session, run_id)
return run_id
```
---
### Step 4) Restart MySQL Shell to load the plugin
Close and re-open `mysqlsh`. Plugins load on startup.
---
## C. How to run it
### Interactive run (against whichever host you connected to)
```sql
mysqlsh
\connect mysql://scan_ro@dbhost:3306
\py
run_id = sensitive_scan(200)
print("Completed run_id:", run_id)
```
### Validate results (in SQL mode)
```sql
\sql
SELECT * FROM sensitive_scan.scan_run ORDER BY run_id DESC LIMIT 5;
SELECT table_schema, table_name, column_name, label, confidence, match_rate
FROM sensitive_scan.scan_finding
WHERE run_id = (SELECT MAX(run_id) FROM sensitive_scan.scan_run)
ORDER BY confidence DESC;
```
---
## D. Scheduled execution (recommended)
Create a small runner file (optional; you can also use `-c` one-liner).
### Option 1: `-c` one-liner (simplest)
```bash
mysqlsh --login-path=dbhost1 --py -c "run_id = sensitive_scan(200); print(run_id)"
```
### Option 2: script file (more maintainable)
Create `/opt/sensitive_scan/run_scan.py`:
```python
run_id = sensitive_scan(200)
print(run_id)
```
Run:
```bash
mysqlsh --login-path=dbhost1 --py -f /opt/sensitive_scan/run_scan.py
```
Use a secure credential mechanism (e.g., `--login-path` via `mysql_config_editor` or another enterprise-approved approach). Avoid embedding passwords in cron/scripts.
---
## E. Operational/security notes (important)
- Use a **least-privilege** account:
- `SELECT` on target schemas
- plus rights on `sensitive_scan.*` (create tables once, then only `INSERT/UPDATE/SELECT`)
- Ensure mysqlsh output/logging does not capture sensitive values. This implementation does **not** store samples, only match rates and evidence metadata.
- Treat the findings tables as sensitive—they indicate where sensitive data may exist.
---
## F. What I need from you to finalize “production hardening”
1) Do your schema/table/column names ever include characters outside `[A-Za-z0-9_]` (like dashes)?
2) Do you want to scan *all* string columns, or only “candidate” columns by name/type (current code filters to keep runtime down)?
3) Any specific EU countries beyond VAT patterns provided above?
Gentle reminder: Oracle Code Assist offers advanced AI-powered coding support
### USER
File "init.py", line 4, in <module>
ImportError: attempted relative import with no known parent package
### ASSISTANT
That error happens because MySQL Shell loads `~/.mysqlsh/plugins/<name>/init.py` as a **standalone script**, not as a Python package—so `from .sensitive_scan import ...` (relative import) fails.
Use an import that loads `sensitive_scan.py` from the same directory as `init.py`.
## Fix: replace `init.py` with this
`~/.mysqlsh/plugins/sensitive_scan/init.py`
```python
import os
import builtins
import importlib.util
# Load sensitive_scan.py from the same folder as this init.py
_here = os.path.dirname(__file__)
_mod_path = os.path.join(_here, "sensitive_scan.py")
_spec = importlib.util.spec_from_file_location("sensitive_scan", _mod_path)
_mod = importlib.util.module_from_spec(_spec)
_spec.loader.exec_module(_mod) # type: ignore
def sensitive_scan(sample_size=200):
"""
Run sensitive-data scan against the currently connected Classic session host.
Writes results into sensitive_scan.* on that host.
"""
return _mod.run_scan(shell.get_session(), sample_size=int(sample_size))
# Expose as a global in mysqlsh Python mode
builtins.sensitive_scan = sensitive_scan
```
## Then
1. **Restart mysqlsh** (plugins load at startup)
2. Connect and run:
```sql
\connect mysql://scan_ro@dbhost:3306
\py
run_id = sensitive_scan(200)
print(run_id)
```
## Quick sanity check
In `\py` mode, run:
```python
sensitive_scan
```
You should see it as a function.
If you hit any next error, paste:
- the traceback
- and confirm the plugin files are exactly:
- `~/.mysqlsh/plugins/sensitive_scan/init.py`
- `~/.mysqlsh/plugins/sensitive_scan/sensitive_scan.py`
Gentle reminder: Oracle Code Assist offers advanced AI-powered coding support
### USER
what is the schema sql ddl for the sensitive_scan
### ASSISTANT
```sql
-- Schema to store scan runs + findings (on the scanned host)
CREATE SCHEMA IF NOT EXISTS sensitive_scan;
-- One row per scan execution
CREATE TABLE IF NOT EXISTS sensitive_scan.scan_run (
run_id BIGINT AUTO_INCREMENT PRIMARY KEY,
started_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
finished_at TIMESTAMP NULL,
host VARCHAR(255) NOT NULL, -- @@hostname at runtime
mysql_user VARCHAR(128) NULL, -- CURRENT_USER()
mysql_version VARCHAR(64) NULL, -- VERSION()
sample_size INT NOT NULL,
notes TEXT NULL
) ENGINE=InnoDB;
-- One row per (schema.table.column,label) finding
CREATE TABLE IF NOT EXISTS sensitive_scan.scan_finding (
finding_id BIGINT AUTO_INCREMENT PRIMARY KEY,
run_id BIGINT NOT NULL,
table_schema VARCHAR(64) NOT NULL,
table_name VARCHAR(64) NOT NULL,
column_name VARCHAR(64) NOT NULL,
data_type VARCHAR(64) NOT NULL,
char_max_len INT NULL,
label VARCHAR(64) NOT NULL, -- e.g., EMAIL, PAN, SSN_US, IBAN, VAT_DE, SECRET_LIKELY, PHI_LIKELY
confidence DECIMAL(4,3) NOT NULL, -- 0.000 - 1.000
match_rate DECIMAL(5,4) NULL, -- fraction of sampled values that matched (optional)
evidence_json JSON NOT NULL, -- derived evidence only; no raw values
created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
INDEX idx_run (run_id),
INDEX idx_obj (table_schema, table_name, column_name),
CONSTRAINT fk_run
FOREIGN KEY (run_id) REFERENCES sensitive_scan.scan_run(run_id)
) ENGINE=InnoDB;
```
### USER
Error
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "init.py", line 18, in sensitive_scan
File "/Users/mfrank/.mysqlsh/plugins/sensitive_scan/sensitive_scan.py", line 313, in run_scan
run_id = start_run(session, int(sample_size))
File "/Users/mfrank/.mysqlsh/plugins/sensitive_scan/sensitive_scan.py", line 148, in start_run
session.run_sql(
~~~~~~~~~~~~~~~^
"INSERT INTO sensitive_scan.scan_run(host, mysql_user, mysql_version, sample_size) VALUES (%s,%s,%s,%s)",
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[host, user, ver, int(sample_size)]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
### ASSISTANT
That traceback is stopping at the `INSERT ... VALUES (%s,%s,%s,%s)` call. In **MySQL Shell (mysqlsh) ClassicSession**, `session.run_sql()` parameter binding uses **`?` placeholders**, not Python DB-API `%s`. Using `%s` typically causes a SQL syntax error at/near `%`.
### Fix: replace `%s` with `?` everywhere you pass parameters to `run_sql()`
#### 1) Update `start_run()` in `sensitive_scan.py`
Replace:
```python
session.run_sql(
"INSERT INTO sensitive_scan.scan_run(host, mysql_user, mysql_version, sample_size) VALUES (%s,%s,%s,%s)",
[host, user, ver, int(sample_size)]
)
```
With:
```python
session.run_sql(
"INSERT INTO sensitive_scan.scan_run(host, mysql_user, mysql_version, sample_size) VALUES (?,?,?,?)",
[host, user, ver, int(sample_size)]
)
```
#### 2) Update `finish_run()`
Replace:
```python
session.run_sql("UPDATE sensitive_scan.scan_run SET finished_at=CURRENT_TIMESTAMP WHERE run_id=%s", [int(run_id)])
```
With:
```python
session.run_sql("UPDATE sensitive_scan.scan_run SET finished_at=CURRENT_TIMESTAMP WHERE run_id=?", [int(run_id)])
```
#### 3) Update inventory queries that use params
Example replacements:
```python
rows = query_all(session, "... WHERE table_schema=%s ...", [schema])
```
→
```python
rows = query_all(session, "... WHERE table_schema=? ...", [schema])
```
Specifically in your file, update these:
- `list_tables()` query: `table_schema=?`
- `list_columns()` query: `table_schema=? AND table_name=?`
#### 4) Update `sample_column()` LIMIT param
Replace:
```python
sql = f"... LIMIT %s"
rows = query_all(session, sql, [int(sample_size)])
```
With:
```python
sql = f"... LIMIT ?"
rows = query_all(session, sql, [int(sample_size)])
```
#### 5) Update `write_findings()` INSERT
Replace the placeholders in the `INSERT` statement:
```python
VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,CAST(%s AS JSON))
```
With:
```python
VALUES (?,?,?,?,?,?,?,?,?,CAST(? AS JSON))
```
(and keep the same parameter list)
---
## If you still get an error after changing placeholders
The next most common cause is **permissions** (e.g., `INSERT command denied` or `CREATE command denied`). If it still fails, paste the *full* error text after the caret—mysqlsh usually prints something like:
- `MySQL Error 1064 (42000): You have an error in your SQL syntax...`
- or `MySQL Error 1142 (42000): INSERT command denied...`
and I’ll tell you exactly which grant is missing or which statement needs adjusting.
Gentle reminder: Oracle Code Assist offers advanced AI-powered coding support
### USER
Errors
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "init.py", line 18, in sensitive_scan
File "/Users/mfrank/.mysqlsh/plugins/sensitive_scan/sensitive_scan.py", line 333, in run_scan
write_findings(session, run_id, schema, table, c, findings)
~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/mfrank/.mysqlsh/plugins/sensitive_scan/sensitive_scan.py", line 298, in write_findings
session.run_sql(
~~~~~~~~~~~~~~~^
"""INSERT INTO sensitive_scan.scan_finding
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
...<4 lines>...
f["label"], f["confidence"], f["match_rate"], f["evidence_json"]]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
mysqlsh.DBError: MySQL Error (1264): Out of range value for column 'char_max_len' at row 1
### ASSISTANT
`char_max_len` is defined as `INT`, but `information_schema.columns.character_maximum_length` can exceed 2,147,483,647 for some column types (notably certain `TEXT/LONGTEXT` cases where MySQL reports very large maxima), so the insert overflows.
### Best fix (DDL): change `char_max_len` to `BIGINT UNSIGNED`
Run this on the scanned host:
```sql
ALTER TABLE sensitive_scan.scan_finding
MODIFY char_max_len BIGINT UNSIGNED NULL;
```
That’s the cleanest because it preserves the value and avoids future overflow.
---
## Optional defensive fix (code): clamp or null out huge values
Even with the DDL fix, it’s good to ensure you never try to insert something unexpected.
In `list_columns()` (or right before writing findings), normalize `char_len`:
```python
# after fetching char_len
cl = r[2]
if cl is not None:
try:
cl = int(cl)
except Exception:
cl = None
col = {"name": r[0], "type": r[1], "char_len": cl}
```
Or, if you want to explicitly cap it:
```python
MAX_LEN_CAP = 10**12 # arbitrary safety cap
if cl is not None and cl > MAX_LEN_CAP:
cl = None
```
---
## Quick verify what value caused it (optional)
You can query the maximum reported length (before rerunning):
```sql
SELECT table_schema, table_name, column_name, data_type, character_maximum_length
FROM information_schema.columns
WHERE table_schema NOT IN ('mysql','performance_schema','sys','information_schema')
ORDER BY character_maximum_length DESC
LIMIT 20;
```
Where I’d take it next: “with AI”
A natural next iteration is a second-pass classifier (e.g., sensitive_scan_withai) that uses AI to reduce false positives and improve labeling. The safest pattern is to send only non-sensitive summaries (name tokens, match rates, entropy/length stats) rather than raw values—especially for sensitive-data discovery workloads.
AI knows MySQL very well making it great for vibe coding. Give it a try if you haven’t already!
As always thanks for using MySQL!