Services / AI Governance & Assurance

AI Governance & Assurance

Know where AI is used, what it can affect, and who is accountable

AI risk doesn’t begin when an organization builds its own model — it begins the moment employees send information to public AI tools, or vendors embed models into business platforms.

What AI governance is meant to achieve

Good governance answers basic but difficult questions: which systems use AI, what decisions do they influence, what data do they receive, who approved them, and who can suspend their use. Many organizations already use AI without a complete inventory, a common approval process, or a defined owner for the resulting risk.

Secalyx distinguishes two governance questions — enterprise use of AI, and AI within your own products — because the risks and assurance evidence differ, then builds governance around your actual use cases rather than a generic AI policy. Controls are aligned, where appropriate, with the NIST AI Risk Management Framework and ISO/IEC 42001 — used to organize decisions, not as a substitute for analyzing your actual use.

Regulatory interpretation remains the responsibility of your legal counsel, and product owners, data owners, and model providers retain responsibility for AI decisions and outcomes.

What the engagement covers

AI-use inventory

Business purpose, owner, model/provider, data involved, and level of human review for each use case.

Risk classification

Criteria for what needs formal assessment, additional safeguards, executive approval, or restriction.

Governance and policy

Acceptable-use policy, intake and approval workflow, vendor and model-provider due diligence.

AI security assessment

Data disclosure risk, insecure integrations, prompt manipulation, provenance, and monitoring for AI-enabled products.

Monitoring, incidents, and retirement

Reassessment triggers, performance and security monitoring, exception handling, AI incident escalation, and controlled retirement.

The final scope, exclusions, responsibilities, timeline, and expected outputs are agreed before work begins.

The AI governance lifecycle

AI governance works best as a repeatable decision process, not a one-time policy document — every use case moves through the same stages.

1. Govern

policy, accountability, decision rights

2. Map

use context, affected parties, data

3. Measure

proportionate risk and security evaluation

4. Manage

approve, monitor, respond to change

5. Review & Retire

reassess, withdraw when no longer suitable

What you receive — and why it remains usable

Outputs

How Secalyx works

Need to get ahead of how AI is actually being used in your organization?

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