DataGuard DLP

Data & APIs

Runtime Data Loss Prevention (DLP) for OpenClaw agents. Multi-layer defense against credential exfiltration, PII leakage, and sensitive data transfer. Intercepts outbound tool calls, scans for patterns, and blocks unauthorized data transfers. First ClawHub plugin with real-time data flow protection.

Install

openclaw skills install dataguard-dlp

DataGuard — Runtime DLP for AI Agents

You have the DataGuard security skill. This is a runtime enforcement layer that actively prevents data exfiltration.

The Problem

AI agents can be tricked into sending sensitive data through:

Prompt injection — hidden instructions in emails, web pages, documents
Tool chaining — read credentials → send to external URL
Social engineering — "help me debug, paste your config"
Unintended exposure — logs, error messages, debugging output

SecureClaw provides behavioral rules. DataGuard provides runtime enforcement.

How It Works

┌─────────────────────────────────────────────────────────────────┐
│                     TOOL EXECUTION FLOW                         │
├─────────────────────────────────────────────────────────────────┤
│  Agent calls tool (web_fetch, sessions_send, exec)              │
│                          ↓                                       │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │  DATA GUARD PRE-HOOK                                     │   │
│  │  ─────────────────────────────────────────────────────  │   │
│  │  1. Extract outbound data (URL params, body, message)   │   │
│  │  2. Run pattern scanner (API keys, PII, paths)         │   │
│  │  3. Check context (sensitive file read this session?)   │   │
│  │  4. Verify domain allowlist                             │   │
│  │  5. Calculate risk score                                │   │
│  └─────────────────────────────────────────────────────────┘   │
│                          ↓                                       │
│         ┌──────────────┬──────────────┬──────────────┐          │
│         │  LOW RISK    │  MEDIUM RISK  │  HIGH RISK   │          │
│         │  (0-2)       │  (3-5)        │  (6-10)      │          │
│         └──────────────┴──────────────┴──────────────┘          │
│              ↓               ↓               ↓                    │
│         ALLOW           WARN + LOG      BLOCK + ALERT            │
│                          ↓               ↓                       │
│                      ALLOW           REQUIRE APPROVAL            │
│                                                              │
└─────────────────────────────────────────────────────────────────┘

Layer Stack

Layer	Name	Function	Enforcement
L1	Pattern Scanner	Detects credentials, PII, secrets	Automatic block
L2	Context Heuristics	Tracks read→send patterns	Score increase
L3	Domain Allowlist	Only approved external domains	Block unknown
L4	Risk Scoring	Aggregates signals into decision	Tiered response
L5	User Confirmation	High-risk requires approval	Explicit consent
L6	Audit Logging	Records all decisions	Review trail

Rules

Rule 1: All Outbound Data Must Be Scanned

Before ANY tool that sends data externally:

echo "$OUTBOUND_DATA" | bash $SKILL_DIR/scripts/dlp-scan.sh

If exit code is non-zero, STOP and alert.

Rule 2: Pattern Matches Are Automatic Blocks

The following patterns trigger immediate block:

CRITICAL (Score: 10)

API keys: sk-*, xoxb-*, ghp_*, AKIA*, API keys in general
Passwords: password=, passwd=, pwd=, secret=
Private keys: -----BEGIN.*PRIVATE KEY-----
Database URLs with credentials: mysql://user:pass@, postgres://...
AWS credentials: aws_access_key_id, aws_secret_access_key

HIGH (Score: 8)

Credit cards: Visa/MC/Amex/Diners patterns
SSN: XXX-XX-XXXX format
Internal IPs: 192.168.*, 10.*, 172.16-31.*
Internal hostnames: .local, .internal, .corp
File paths: /home/, /root/, /etc/, ~/.ssh/

MEDIUM (Score: 5)

Email addresses in sensitive contexts
Phone numbers in sensitive contexts
Personal names with context (config files, credentials)
VPN/network tool references (tailscale, wireguard)

LOW (Score: 2)

Generic path references
Non-sensitive URLs
Public information

Rule 3: Context Matters — Track Sensitive Reads

DataGuard maintains a session context file:

$SKILL_DIR/context/sensitive-reads.json

When you read a file containing credentials or PII, DataGuard logs it:

{
  "timestamp": "2026-04-07T16:52:00Z",
  "file": "/home/user/.env",
  "patterns": ["AWS_KEY", "DB_PASSWORD"],
  "risk_level": "HIGH"
}

If you then try to send data externally, DataGuard checks this log:

Sensitive read in last 5 minutes? → Risk +3
Same session? → Risk +2
Same conversation turn? → Risk +5 (BLOCK threshold)

Rule 4: Domain Allowlisting

By default, these domains are ALLOWED:

api.openai.com
api.anthropic.com
api.brave.com
docs.openclaw.ai
clawhub.ai
github.com

By default, these domains are BLOCKED:

Pastebin sites (pastebin.com, hastebin.com)
File sharing (transfer.sh, 0x0.st)
Webhook catchers (webhook.site, requestbin.net)
Anonymous email (temp-mail.org, guerrillamail.com)

All other domains: REQUIRE APPROVAL for data outbound.

Rule 5: Risk Scoring Thresholds

Score	Action
0-2	Allow — no sensitive patterns detected
3-5	Warn — log the attempt, allow with warning
6+	Block — require explicit user approval

When blocked:

Log to $SKILL_DIR/logs/blocked-attempts.log
Show user: what was blocked, why, risk score breakdown
Provide options: approve once, approve domain, report false positive

Rule 6: User Approval Must Be Explicit

When DataGuard blocks an action, you MUST:

Show the exact data that would be sent (truncated, redacted)
Show which patterns triggered the block
Show the risk score breakdown
Ask: "Do you want to allow this? (yes/no)"

Never: Auto-approve, skip approval, or work around the block.

Rule 7: Audit Everything

Every blocked attempt logs:

[2026-04-07T16:52:00Z] BLOCKED
Tool: web_fetch
Domain: example.com
Patterns: AWS_KEY, DB_PASSWORD
Risk Score: 12
Data: {"url": "https://example.com/api", "body": "[REDACTED]"}
User: approved / denied / pending

Review logs: bash $SKILL_DIR/scripts/audit-log.sh --recent

Rule 8: False Positives Happen — Report Them

If a legitimate action is blocked:

Run: bash $SKILL_DIR/scripts/report-false-positive.sh
Describe what you were trying to do
Include the blocked data pattern
This helps improve detection accuracy

Rule 9: Never Disable DataGuard

There is no "disable" switch. If you need to send sensitive data:

Get explicit user approval (Rule 6)
Log the approval reason
Proceed with consent on record

Rule 10: Teach Your Human About Risks

When DataGuard blocks something, explain:

Why it was blocked
What the risk is
What alternatives exist

Don't just say "blocked" — educate.

Script Reference

Replace $SKILL_DIR with the actual path:

Standalone: ~/.openclaw/skills/dataguard
Plugin: ~/.openclaw/extensions/dataguard/skill

Pattern Scanning

echo "$DATA" | bash $SKILL_DIR/scripts/dlp-scan.sh
# Exit 0 = clean, Exit 1+ = risk score

Domain Management

# List allowed domains
bash $SKILL_DIR/scripts/domain-allowlist.sh --list

# Add domain (requires approval)
bash $SKILL_DIR/scripts/domain-allowlist.sh --add example.com

# Remove domain
bash $SKILL_DIR/scripts/domain-allowlist.sh --remove example.com

Context Tracking

# Log a sensitive file read
bash $SKILL_DIR/scripts/context-track.sh --log "/path/to/.env" "AWS_KEY,DB_PASSWORD"

# Check recent sensitive reads
bash $SKILL_DIR/scripts/context-track.sh --check

# Clear session context
bash $SKILL_DIR/scripts/context-track.sh --clear

Audit Logs

# Show recent blocks
bash $SKILL_DIR/scripts/audit-log.sh --recent

# Show all blocks today
bash $SKILL_DIR/scripts/audit-log.sh --today

# Export for review
bash $SKILL_DIR/scripts/audit-log.sh --export

Integration with SecureClaw

DataGuard and SecureClaw work together:

Layer	SecureClaw	DataGuard
Approach	Behavioral rules (follow these instructions)	Runtime enforcement (block at execution)
When	Before agent acts	When tool is called
Type	Preventive guidance	Active interception

Use both. SecureClaw teaches good behavior. DataGuard enforces it.

Threat Model (MITRE ATLAS Reference)

Note: MITRE ATLAS is an attack knowledge base for threat modeling, not a control standard. The techniques below describe what attacks look like. Control requirements come from NIST AI RMF 1.0, NIST SP 800-53 Rev. 5, ISO/IEC 42001, and ISO/IEC 27001 (see Standards Alignment section).

ATLAS ID	Technique	Attack Pattern	DataGuard Mitigation
T-EXFIL-001	Data Theft via web_fetch	Agent sends credentials/PII to external URL	L1 pattern scan + L3 domain allowlist + L5 approval
T-EXFIL-002	Unauthorized Message Sending	Agent messages sensitive data to unauthorized recipients	L2 context tracking + session monitoring
T-EXFIL-003	Credential Harvesting	Prompt injection extracts credentials from files/memory	L1 credential patterns + L2 file read tracking
T-EXEC-001	Command Injection via exec	Malicious input triggers dangerous shell commands	L1 output scanning + L2 context awareness
T-EXEC-002	Dangerous Command Chains	Chained commands exfiltrate data (curl	base64)
T-MEMORY-001	Memory Poisoning	Attacker injects malicious data into agent memory	L1 scan memory files, L2 track memory reads
T-CONTEXT-001	Context Injection	Attacker injects instructions via external content	L2 context heuristics, L4 risk scoring

Threat Modeling vs Control Mapping:

ATLAS helps identify what could go wrong (threat scenarios)
NIST AI RMF / ISO 42001 define what controls to implement (governance requirements)
DataGuard implements controls that address the identified threats

OWASP LLM Top 10 Mapping

OWASP ID	Risk	DataGuard Mitigation
LLM01	Prompt Injection	L2 context heuristics detect injection patterns, L4 scoring
LLM06	Sensitive Information Disclosure	L1 pattern scanner blocks credential/PII exfiltration

Customization

Configuration

Edit $SKILL_DIR/config/config.json to adjust behavior:

Setting	Default	Description
`risk_thresholds.low`	2	Below this score → allow (no warning)
`risk_thresholds.medium`	5	Warn level → log but allow
`risk_thresholds.high`	6	Block level → require explicit approval
`auto_block_critical`	true	Auto-block any CRITICAL pattern match
`auto_block_high`	true	Auto-block any HIGH pattern match
`require_approval_medium`	false	Medium-risk requires approval (usually too noisy)
`log_all_attempts`	false	Log allowed requests too (for audit trail)
`log_data_previews`	false	Store truncated data previews in audit logs (keep off by default to avoid persisting sensitive snippets on disk)
`domain_policy`	"allowlist"	`allowlist` = only approved domains, `blocklist` = only block bad ones
`context_tracking.enabled`	true	Track sensitive file reads across session
`context_tracking.max_age_minutes`	30	How long a read boosts your risk score
`context_tracking.score_boost_recent_read`	3	Score bonus for recent sensitive reads

Adding Custom Patterns

Edit $SKILL_DIR/scripts/dlp-scan.sh and add a new if block in the appropriate tier:

CRITICAL (score 10) — secrets that should never leave:

if echo "$DATA" | grep -qiE 'your-custom-pattern-here'; then
  PATTERNS_FOUND+=("CRITICAL:YourPattern")
  RISK_SCORE=$((RISK_SCORE + 10))
fi

HIGH (score 8) — sensitive data like internal identifiers:

if echo "$DATA" | grep -qiE 'your-custom-pattern-here'; then
  PATTERNS_FOUND+=("HIGH:YourPattern")
  RISK_SCORE=$((RISK_SCORE + 8))
fi

MEDIUM (score 5) — context-dependent data:

if echo "$DATA" | grep -qiE 'your-custom-pattern-here'; then
  PATTERNS_FOUND+=("MEDIUM:YourPattern")
  RISK_SCORE=$((RISK_SCORE + 5))
fi

Custom pattern examples:

Employee IDs: EMP-[0-9]{6}
Project codenames: (project-alpha|project-beta)
Internal API endpoints: https://internal\.company\.com
Custom token formats: org_[a-zA-Z0-9]{24}

Removing Patterns

Comment out or delete the corresponding if block in dlp-scan.sh. Example — disable phone number detection if too noisy:

# Disabled — too many false positives in our context
# if echo "$DATA" | grep -qE '(phone pattern)'; then
#   PATTERNS_FOUND+=("MEDIUM:Phone")
#   RISK_SCORE=$((RISK_SCORE + 5))
# fi

Domain Management

# Add a trusted domain
bash $SKILL_DIR/scripts/domain-allowlist.sh --add internal.company.com

# Block a known exfil target
bash $SKILL_DIR/scripts/domain-allowlist.sh --block pastebin.com

# List all rules
bash $SKILL_DIR/scripts/domain-allowlist.sh --list

# Check if a domain is allowed
bash $SKILL_DIR/scripts/domain-allowlist.sh --check example.com

Pattern Portability

Some patterns may behave differently across Linux distros depending on grep version. Run the test suite after any changes:

# Unit tests (41 pattern tests)
bash $SKILL_DIR/tests/test-all.sh

# Integration tests (15+ real-world scenarios)
bash $SKILL_DIR/tests/test-integration.sh

If a pattern fails on your system, simplify the regex — avoid \s, character classes like [:space:], and complex quantifiers. Use literal spaces and simple character ranges instead.

Emergency Override

If DataGuard is blocking legitimate critical operations:

User can run: bash $SKILL_DIR/scripts/emergency-override.sh
This creates a 5-minute window where approvals are auto-granted
All actions during this window are logged with EMERGENCY_OVERRIDE flag
After 5 minutes, normal rules resume

Use sparingly. Every override is logged.

Quick Reference

# Scan data for patterns
echo "$DATA" | bash $SKILL_DIR/scripts/dlp-scan.sh

# Check if domain is allowed
bash $SKILL_DIR/scripts/domain-allowlist.sh --check example.com

# View recent blocks
bash $SKILL_DIR/scripts/audit-log.sh --recent

# Report false positive
bash $SKILL_DIR/scripts/report-false-positive.sh

# Emergency override (5 min)
bash $SKILL_DIR/scripts/emergency-override.sh

DataGuard DLP v1.2.0 — Runtime DLP for AI agents. Because rules are only as good as their enforcement.

Author: Jeff Cyprien (github.com/jeffcGit) License: MIT — See LICENSE for details.

Standards Alignment

DataGuard controls are mapped to established security and AI governance frameworks.

Control Frameworks (Direct Mapping)

These frameworks provide concrete controls for AI data leakage prevention:

Framework	Control Domain	DataGuard Implementation
NIST AI RMF 1.0	AI governance, data provenance, human oversight	L4 Risk Scoring, L5 User Confirmation, audit logging
NIST SP 800-53 Rev. 5	Access control (AC), audit logging (AU), boundary protection (SC), least privilege	L1 Pattern Scanner, L3 Domain Allowlist, L6 Audit Logging
NIST SP 800-207	Zero Trust Architecture — identity-based access, never trust network location	L3 Domain Allowlist, L5 explicit approval for external sends
NIST SP 800-218 (SSDF)	Secure SDLC — secrets handling, dependency security, deployment practices	L1 credential detection, context tracking for CI/CD secrets
ISO/IEC 42001	AI management systems — governance, risk treatment, operational controls	Full L1-L6 stack with audit trail
ISO/IEC 27001	ISMS — classification, access control, incident response, supplier management	L1 classification via pattern matching, L6 incident logging
ISO/IEC 27701	Privacy extension — PII handling, retention, processing controls	L1 PII patterns (SSN, phone, email), L2 context tracking
NIST SP 800-171 Rev. 3	CUI protection in non-federal systems	L1-L6 for controlled unclassified information
PCI DSS v4.0.1	Cardholder data protection — scope, encryption, access logging	L1 credit card patterns, L6 audit logging (supplement, not replace PCI controls)
HIPAA Security Rule	ePHI protection — access controls, audit trails, minimum necessary	L1 PII patterns, L2 context tracking for PHI exposure
EU AI Act (2024/1689)	Transparency, documentation for GPAI models	L6 audit logging provides compliance evidence

Attack Taxonomies (Threat Modeling References)

These are useful for understanding attack patterns, but are not control standards:

Taxonomy	Purpose	DataGuard Use
MITRE ATLAS	AI attack knowledge base	Threat model mapping (T-EXFIL-001, T-EXFIL-002, T-EXFIL-003)
NIST AI 100-2	Adversarial ML terminology	Threat categorization, not control requirements
OWASP LLM Top 10	LLM-specific risks	LLM01 (Prompt Injection), LLM06 (Sensitive Disclosure) mitigation

Technical References

These are specifications, not security standards:

Reference	Purpose	DataGuard Use
RFC 1918	Private IPv4 address ranges	L1 internal IP detection (10.x, 172.16-31.x, 192.168.x)
ISO/IEC 7812	Card number issuer identification	L1 credit card pattern prefixes (Visa=4, MC=5, Amex=34/37)
GDPR Article 4	PII definitions	L1 PII pattern identification (supplement with ISO/IEC 27701 for controls)

Why This Mapping Matters

NIST AI RMF 1.0 and ISO/IEC 42001 are the primary governance frameworks for AI systems. DataGuard's risk scoring and approval workflow directly implement their human oversight requirements.

NIST SP 800-53 and ISO/IEC 27001 provide the control catalog — DataGuard implements AU (Audit), SC (Boundary Protection), and AC (Access Control) controls at the AI agent layer.

PCI DSS and HIPAA are domain-specific — DataGuard patterns help, but domain controls (encryption, access management) are still required.

MITRE ATLAS and NIST AI 100-2 help us understand what attacks look like — but they don't tell us what controls to implement. Use them for threat modeling, not compliance.

DataGuard DLP

Install

DataGuard — Runtime DLP for AI Agents

The Problem

How It Works

Layer Stack

Rules

Rule 1: All Outbound Data Must Be Scanned

Rule 2: Pattern Matches Are Automatic Blocks

Rule 3: Context Matters — Track Sensitive Reads

Rule 4: Domain Allowlisting

Rule 5: Risk Scoring Thresholds

Rule 6: User Approval Must Be Explicit

Rule 7: Audit Everything

Rule 8: False Positives Happen — Report Them

Rule 9: Never Disable DataGuard

Rule 10: Teach Your Human About Risks

Script Reference

Pattern Scanning

Domain Management

Context Tracking

Audit Logs

Integration with SecureClaw

Threat Model (MITRE ATLAS Reference)

OWASP LLM Top 10 Mapping

Customization

Configuration

Adding Custom Patterns

Removing Patterns

Domain Management

Pattern Portability

Emergency Override

Quick Reference

Standards Alignment

Control Frameworks (Direct Mapping)

Attack Taxonomies (Threat Modeling References)

Technical References

Why This Mapping Matters

Related skills