AI Security Implementation
Implement the technical controls required to protect data, models, RAG, inference endpoints, agents, tools and AI operations.
What this capability solves
Architecture recommendations only reduce risk after controls are engineered into the platform and validated in production-like conditions.
Technology is implemented as an operating capability: architecture, integration, governance, assurance, people, procedures and measurable outcomes are designed together.
Capability model
Modular building blocks allow the scope to start with a focused pilot and expand into an enterprise operating model.
Data Protection
Classification, redaction, access control, secure retrieval and sensitive-data handling.
Model / Artifact Security
Registry controls, signatures, supply-chain scanning and integrity protections.
LLM Gateway
Authentication, policy, rate limits, content controls, prompt protections and audit.
RAG Controls
Source allowlists, retrieval authorization, segmentation and indirect-injection safeguards.
Agent Security
Workload identity, scoped tools, sandboxing, approval gates and transaction limits.
AI Monitoring
Security telemetry, anomaly detection, response playbooks, kill switch and evidence.
How the capability fits together
Final topology, control placement and deployment model are validated during discovery and detailed design.
Controls & governance
- Named business and technical owner
- Use-case risk classification and approval gates
- Data provenance, minimization and access control
- Human accountability for high-impact outcomes
- Security and privacy-by-design controls
- Versioned model/prompt/agent configuration
- Pre-release evaluation and red-team gates
- Continuous monitoring, incident and change control
- Audit-ready evidence and management reporting
Priority use cases
- Secure GenAI deployment
- Private RAG
- AI gateway rollout
- Agentic AI platform
- MLOps security hardening
- AI security remediation
Key deliverables
- Implementation design
- Configuration baseline
- Gateway/RAG/agent controls
- Security test cases
- Monitoring integration
- Runbooks
- Handover pack
Integration considerations
- Enterprise IAM and workload identity
- Data lake/warehouse and vector/RAG platforms
- Model/API providers and private models
- Application/API integration layer
- MLOps/LLMOps/AgentOps and observability
- SIEM/SOAR and security tooling
- GRC, privacy and evidence repositories
- ITSM/BPM and business workflow systems
Phased delivery
Each phase ends with evidence, acceptance criteria and a decision gate before broader scale-out.