AI Policy & Guideline Development
Create practical AI policies, standards and user/engineering guidelines that translate governance principles into enforceable behavior and delivery requirements.
What this capability solves
High-level AI principles do not tell employees, engineers or vendors what is permitted. Policies need clear boundaries for data use, public AI, model selection, agent actions, human oversight, logging and exceptions.
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.
Enterprise AI Policy
Purpose, principles, scope, roles, prohibited/restricted uses and accountability.
Acceptable Use
Rules for public GenAI, sensitive data, approved tools, intellectual property and confidential information.
Engineering Standard
Data, model, RAG, agent, evaluation, logging, security and release requirements.
Third-Party AI Rules
Due diligence, data-use clauses, provider risk, retention, residency and assurance.
Agentic AI Standard
Tool permissions, workload identity, approval gates, memory, kill switch and transaction boundaries.
Exception Process
Documented business justification, compensating controls, expiry and re-approval.
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
- Public GenAI rollout
- Internal assistant governance
- AI engineering standardization
- Third-party AI procurement
- Agentic automation
- Policy refresh after regulatory change
Key deliverables
- AI policy
- Acceptable-use standard
- AI engineering standard
- Agentic AI control standard
- Vendor AI clause set
- Exception workflow
- Awareness materials
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.