LLM Security & Compliance Hub
Trust & risk for EU/enterprise: privacy, PII, prompt injection
This hub provides patterns for observable systems that stay compliant: logging without PII, redaction, retention, and security threats for RAG. For buyers who need data boundaries and auditability.
Common security & compliance pains
Pain page
Need observability without exposing prompt data?
Tracing, drift alerts, and logging design for diagnosis without raw-content sprawl.
Pain page
Need release controls for safety and policy regressions?
Use the regression-testing path for guardrail drift, policy gates, and rollout control.
New playbook
Prompt Injection in RAG: What to Test, What to Block, What to Log
A practical control plan: attack cases, server-side blocks, and the minimum logging trail for review.
LLM/RAG security threats (prompt injection, exfil)
RAG security: know the threat model before building controls.
Prompt injection
Malicious input manipulates model behavior. Instruction override, jailbreak, or context poisoning in RAG.
Mitigation: Input validation, output filtering, sandboxing. Prompt injection mitigation: detect and block suspicious patterns.
Data exfiltration
RAG retrieves and surfaces sensitive docs to unauthorized users. Data exfiltration in RAG: retrieval over-fetches, no access control on context.
Mitigation: Access control at retrieval, document-level permissions, audit retrieval by user.
PII leakage
Model outputs or logs contain PII. Training data, user input, or context bleeds into responses.
Mitigation: PII redaction at ingestion and output. How to redact PII from LLM logs: pattern-based + allowlists.
Model extraction
Adversary queries to extract model weights or training data. Less common for API-based LLMs.
Mitigation: Rate limits, anomaly detection, monitoring for extraction patterns.
Observability without storing prompts
Log what you need for debugging and SLOs—without PII. Patterns for compliance-friendly observability.
Observability without storing prompts
Log metadata: request ID, latency, model, token count, error code. Avoid logging raw prompts and responses. Use hashes or sampling for debugging.
Structured logs, minimal fields
Only log what you need for debugging and SLOs. Define allowlist of safe fields. No free-text from users.
Sampling strategy
Sample a subset for detailed inspection. Retain samples under retention policy. Delete after use.
Trace without content
Distributed tracing with span metadata (timing, service, status). No prompt/response in trace payloads.
How to redact PII from LLM logs
PII redaction, data retention policies, access control. Keep data boundaries clear.
PII redaction in LLM logs
Pattern-based: emails, SSN, credit cards, names. Allowlists for known-safe values. Redact at ingestion before storage.
Retention policies
Data retention policies: define max retention per log type. Auto-delete. Audit trails for deletion.
Access control
Field-level and log-level access. Least privilege. Who can see what. Audit access to sensitive logs.
Encryption
Encrypt at rest and in transit. Key management. Separate keys per environment or tenant.
Risk checklist
Different risks for vendor (API) vs in-house (self-hosted). Know what to check.
Vendor (API, hosted)
Risks
- Data sent to third party
- Vendor retention and deletion
- Subprocessors
- Jurisdiction
Checklist
- DPA, SOC2, certifications
- Data processing location
- Opt-out of training
- Deletion on request
In-house (self-hosted)
Risks
- Infra access
- Model provider terms
- Internal access controls
- Incident response
Checklist
- Access logs
- Model license compliance
- Network segmentation
- Runbooks
Runbooks for security
Security incident runbooks. PII breach response. Prompt injection response. Auditability.
Security incident runbook
Define: detection, containment, eradication, recovery. Who is on-call. Escalation path. Communication plan.
PII breach response
If PII leaks: contain, assess scope, notify (per regulation), document. Have playbook before incident.
Prompt injection response
If injection succeeds: block pattern, rollback if needed, add detection. Postmortem and update guardrails.
Auditability
Log access to logs. Who queried what, when. Audit trail for compliance and forensics.
Evaluation datasets that respect data boundaries
Eval without PII. Controlled environment. Versioned and auditable. Security test cases.
No PII in eval sets
Compliance-friendly evaluation datasets: synthetic or anonymized. No real user data in test suites.
Controlled environment
Eval runs in isolated env. No production data. Clear data boundary between eval and prod.
Versioned, auditable
Eval set versioned. Know what was tested when. Reproducible for audits.
Security test cases
Include prompt injection, PII leakage, policy violation cases in eval. Measure security metrics.
Enterprise & EU
For EU and enterprise buyers: focus on data boundaries, auditability, and compliance-friendly logging practices. Consult legal for jurisdiction-specific requirements.
Security pillars need concrete controls and validation evidence
These two proof assets cover the main buyer concerns on this hub: privacy-safe observability and prompt-injection hardening with evidence a security review can actually inspect.
Need a security & compliance audit?
We help EU and enterprise teams assess LLM/RAG security, PII handling, and compliance-friendly observability.
Start → Fix → Govern
Enforce the Audit → Sprint → Retainer ladder
Enterprise outcomes require a baseline, shipped fixes, then governance. This is the shortest path to measurable quality, controlled cost, and regression prevention.