01
Models & Endpoints
Securing LLM access points: API keys, rate limits, authentication and request auditing.
- API key rotation
- Managed identity
- Model call logging
- Per-user limits
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AI Security
Zero-Trust.
Models, endpoints, RAG and agents: every attack surface documented with concrete controls aligned with CIS and NIST AI RMF.
Control domains
Four attack surfaces, zero blind spot.
Each domain covers risks specific to generative AI in enterprise contexts.
02
RAG & Vector DB
Securing retrieval pipelines: data access, tenant isolation and document-borne injection prevention.
- Vector RBAC
- Tenant isolation
- Chunk sanitization
- Encryption at rest
03
Agents & Orchestration
Autonomous agent security: least privilege, tool sandboxing and execution-chain auditing.
- Least privilege
- Human approval
- Tool sandboxing
- Full traceability
04
Governance & Compliance
Acceptable use policy, model registry, data inventory and AI Act compliance.
- Model registry
- AI policy
- Bias evaluation
- Risk categories
Priority controls
AI quick wins
P1
Prompt injection - Critical
Validate and sanitize every user input before passing it to the model.
P1
Data leakage - Critical
Strictly isolate sensitive data from AI contexts.
P2
Output auditing - High
Log and analyze model responses to detect drift and high-impact hallucinations.
P2
Access rights - High
Apply granular RBAC to every tool exposed to agents.
P3
Model inventory - Medium
Maintain a registry of models, versions, providers and use cases.
P3
Acceptable use - Medium
Publish an AI acceptable-use policy reviewed and accepted by users.
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