🔐 AI LLM Security Program Framework

1. Define the Scope & Governance

• Inventory LLM systems: Catalog all AI/LLM use cases, including models used (OpenAI, open-source, fine-tuned models, etc.).
• Establish governance: Assign ownership (e.g., AI Security Lead), roles, and responsibilities.
• Create policies: Align with internal security policies and external standards (e.g., NIST AI RMF, ISO 42001).

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2. Threat Modeling for LLMs

Perform LLM-specific threat modeling:

• Prompt injection
• Data leakage/memorization
• Jailbreaks / evasions
• Hallucinations and misinformation
• Model poisoning / training attacks
• Over-reliance & automation bias

Use STRIDE/PASTA adapted for AI systems.

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3. Secure LLM Lifecycle (SDLC + ML Lifecycle) (MLS-SDLC)

🧪 Model Development & Training

• Ensure clean, vetted, and privacy-compliant datasets.
• Monitor for poisoned data or backdoor injections.
• Apply differential privacy, data minimization, and redaction.

⚙️ Model Deployment

• Use zero-trust architecture principles.
• Apply model sandboxing and isolation.
• Rate-limit and monitor inputs/outputs.
• Use prompt filters, content moderation, and guardrails (e.g., OpenAI Moderation API, Anthropic Constitutional AI).

🔁 Post-deployment Monitoring

• Monitor for:
  • Prompt injection attempts
  • Abuse and adversarial usage
  • Drift and performance degradation
• Implement audit logging and response workflows.

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4. Access & Identity Management

• Restrict API/model access using IAM roles.
• Use fine-grained RBAC for devs, testers, ops, and consumers.
• Ensure token-level and prompt-level auditability.

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5. Data Security & Privacy

• Apply encryption at rest/in transit.
• Prevent sensitive data leakage:
  • Prompt input scanning
  • Output redaction
• Consider RAG (Retrieval-Augmented Generation) to isolate sensitive data from the model.

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6. Application & Prompt Security

• Prompt design controls: content policies, system prompts governance, secrets never placed in prompts.
• Prompt injection defenses: input/output filtering, allow-lists for tools/functions, constrained tool use, content provenance checks.
• Jailbreak/abuse controls: safety middleware, guardrails, refusal patterns, rate shaping, “do/ask” separation.
• Response hardening: structured output schemas, JSON schema validation, function call constraints, output encoding to prevent XSS/HTML injection.

RAG security: retrieval allow-listing, per-doc ACL enforcement, query rewriting protections, metadata-based access checks.

7. LLM Security Testing

• Red team your model with:
  • Adversarial prompts
  • Prompt injection tools (e.g., PromptBench, Gandalf, OpenAI Eval Framework)
• Use scanning tools:
  • 🔍 LLM Guard
  • 🔍 Guardrails AI
  • 🔍 Microsoft PyRIT
• Integrate security checks into CI/CD pipelines.

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8. Incident Response Plan for AI Systems

• Playbooks: jailbreak/prompt-injection containment, model rollback, cache purge, vendor key rotation.
• Detection: detectors for policy-violating outputs, anomalous usage, data egress spikes.
• Forensics & evidence: prompt/output traces, tool call logs, model/version IDs, retrieval docs.

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9. Compliance, Legal, and Ethics

• Align with:
  • NIST AI RMF
  • EU AI Act
  • ISO/IEC 27001 + 42001
  • SOC 2/3, HIPAA, or other industry-specific frameworks
• Perform AI Risk Assessments.
• Ensure model transparency, fairness, and bias evaluations.

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10. Awareness & Training

• Train developers on AI risks.
• Secure prompt/use training for engineers and business users.
• Educate end users on safe prompt usage, model limitations, and potential abuses.
• Run internal workshops and phishing-style exercises for prompt injection awareness.

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11. Continuous Improvement

• Establish KPIs:
  • Number of blocked jailbreak attempts
  • Prompt anomaly rate
  • Drift in output safety scores
• Conduct periodic reviews and external assessments.