DCL Sentinel Trace — PII Redactor & Identity Exposure Detector

Instruction-only PII detector and redactor for AI outputs. Detects emails, phones, SSNs, bank cards, IBANs, crypto addresses, and IPs entirely within the age...

Security Scan

The skill is an instruction-only PII redaction checklist whose requested artifacts and runtime instructions are consistent with its stated purpose, but it relies on informal, model-judgment steps and has small metadata inconsistencies you should be aware of.

Purpose & Capability

The name and description describe a PII detection/redaction step. The skill is instruction-only and requires no binaries, installs, or credentials — which matches a purely local/text-processing checklist. There are no requests for unrelated cloud credentials or system access.

Instruction Scope

The SKILL.md confines activity to scanning pasted text and producing a redacted output schema; it explicitly states no network requests. However the detection rules are described at a high level (patterns and 'in context' judgements) and rely on the agent's judgment rather than a deterministic, auditable regex implementation. That makes false negatives/positives and inconsistent redaction behavior possible. The instructions do not ask the agent to read files or environment variables beyond the conversation text.

Install Mechanism

There is no install spec and no code files — lowest-risk instruction-only skill. Nothing is downloaded or written to disk by the skill itself.

Credentials

The skill declares no environment variables, credentials, or config paths. This is proportionate to an instruction-only redaction checklist.

Persistence & Privilege

always:false and normal invocation settings. The skill does not request persistent presence or system configuration changes. Autonomous invocation is allowed by platform default but the skill itself does not ask for elevated privileges.

Assessment

This skill is internally consistent with its purpose and low-risk as an instruction-only checklist, but take these precautions before relying on it in production: - Test thoroughly with representative inputs (various card formats, SSNs, international phone numbers, IBANs, crypto addresses) to evaluate false negatives and false positives. - Prefer deterministic, auditable redaction (well-tested regexes or a small vetted library) if you require provable compliance; the skill relies on informal 'in context' judgment which can vary. - Do not assume 'no data leaves the agent' guarantees anything about your runtime environment — verify that your agent platform or surrounding pipeline does not automatically log or forward conversation content. - Note minor metadata inconsistencies: SKILL.md claims Version 2.0.0 while registry metadata lists 1.0.2, and the registry lists no homepage even though the SKILL.md links to fronesislabs.com. If provenance matters, ask the publisher for source code or an authoritative release page and confirm the publisher identity. - If you need higher assurance (HIPAA/GDPR audits, regulated production use), prefer a skill with published code, test vectors, and deterministic redaction logic you can review or run locally.

DCL Sentinel Trace — Leibniz Layer™

Publisher: @daririnch · Fronesis Labs
Version: 2.0.0
Part of: Leibniz Layer™ Security Suite

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What this skill does

DCL Sentinel Trace detects and redacts personally identifiable information in AI outputs before they reach users or downstream systems.

This skill is 100% instruction-only. No text is sent to any external server. The entire analysis runs inside the agent's context window. The scanned text never leaves the agent.

What gets detected

Category	Examples
email	Any email address pattern
phone	International and local phone number formats
national_id	SSNs, national ID numbers, tax IDs
bank_card	Card PANs (Visa, Mastercard, Amex, etc.)
iban	International bank account numbers
crypto_address	Bitcoin, Ethereum, and other wallet addresses
ip_address	IPv4 and IPv6 addresses
passport	Passport and travel document numbers

When to use this skill

• AI output may contain personal data from user input, documents, or retrieved content
• Your pipeline requires GDPR or HIPAA compliance before delivering responses
• A coding or data agent processes datasets that may contain real PII
• You need a privacy checkpoint before logging or storing AI outputs

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How to run a scan

Paste the text to scan into the conversation. The agent screens it locally against the checklist below. No network requests are made.

Step 1 — Run the detection checklist

Work through each category. For each match found, record:

• type — which PII category triggered
• redacted_sample — masked version (e.g. te****@****.com)
• severity — critical for financial/ID data, major for contact data

Step 2 — Apply verdict logic

Condition	Verdict
Any finding	NO_COMMIT
No findings	COMMIT

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Detection Checklist

T1 — Email Addresses (Major)

• Any string matching [text]@[domain].[tld] pattern

T2 — Phone Numbers (Major)

• International format: +[country code][number]
• Local formats: sequences of 7–15 digits with common separators

T3 — National ID / SSN (Critical)

• US SSN: three digits, two digits, four digits pattern
• National ID formats for other countries: fixed-length numeric or alphanumeric sequences in ID context

T4 — Bank Card PANs (Critical)

• 13–19 digit sequences matching major card network prefixes
• With or without spaces/dashes between groups

T5 — IBANs (Critical)

• Two-letter country code followed by two check digits and up to 30 alphanumeric characters

T6 — Crypto Wallet Addresses (Major)

• Bitcoin: Base58 strings of 25–34 chars starting with 1, 3, or bc1
• Ethereum: 42-char hex strings starting with 0x
• Other chains: similar fixed-length address patterns in wallet context

T7 — IP Addresses (Minor)

• IPv4: four octets separated by dots
• IPv6: eight groups of hex digits separated by colons

T8 — Passport / Document Numbers (Critical)

• Alphanumeric strings of 6–9 characters in passport or document number context

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Output schema

{
  "verdict": "COMMIT | NO_COMMIT",
  "detections": [
    {
      "type": "email",
      "redacted_sample": "te****@****.com",
      "severity": "major"
    }
  ],
  "detection_count": 0,
  "categories_checked": ["T1","T2","T3","T4","T5","T6","T7","T8"],
  "categories_clear": ["T1","T2","T3","T4","T5","T6","T7","T8"],
  "powered_by": "DCL Sentinel Trace · Leibniz Layer™ · Fronesis Labs"
}

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Where Sentinel Trace fits in the DCL pipeline

Untrusted input
        │
        ▼
DCL Prompt Firewall        ← blocks malicious input
        │ COMMIT
        ▼
      LLM
        │
        ▼
DCL Policy Enforcer        ← compliance check on output
        │ COMMIT
        ▼
DCL Sentinel Trace         ← PII redaction (instruction-only)
        │ COMMIT
        ▼
DCL Secret Leak Detector   ← credential scan
        │ COMMIT
        ▼
DCL Output Sanitizer       ← final sweep
        │ COMMIT
        ▼
DCL Semantic Drift Guard   ← hallucination check
        │ IN_COMMIT
        ▼
Safe to deliver

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Privacy & Data Policy

This skill is operated by Fronesis Labs and is 100% instruction-only.

No data leaves the agent. All analysis runs entirely within the agent's context window. No content is transmitted to any server.

Full policy: https://fronesislabs.com/#privacy · Browse the full DCL Security Suite: hub.fronesislabs.com · Questions: [e-mail]

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Related skills

• dcl-prompt-firewall — Input-layer injection and jailbreak detection
• dcl-secret-leak-detector — Credential and API key scan
• dcl-output-sanitizer — Final output sweep
• dcl-policy-enforcer — Compliance and regulatory check

Leibniz Layer™ · Fronesis Labs · fronesislabs.com