Make AI earn authority through evidence
Enterprise AI governance is not a policy document that sits beside delivery. It is the operating system for deciding what may be built, what evidence it must produce, who can approve it, how it is monitored, and when it must be stopped. Altivate brings governance, evaluation, and security into the same release path as the AI product.
A continuous AI assurance loop.
- Govern: Inventory, ownership, risk tier, policy, and authority
- Evaluate: Task quality, safety, robustness, bias, and cost
- Release: Evidence gates, approval, versioning, and rollback
- Monitor: Drift, incidents, misuse, feedback, and re-evaluation
Outcome: AI systems that remain accountable after they enter production
The foundations of operational AI governance
The organization needs one traceable view from use case and model to data, owner, controls, evidence, and production status.
Turn governance principles into release evidence
Frameworks such as the NIST AI RMF are useful when translated into controls teams can execute and verify.
Classify the use case
Define purpose, affected users, decisions, data, jurisdictions, consequences, prohibited uses, and the human authority that remains accountable.
Build the evidence plan
Specify evaluation datasets, metrics, adversarial tests, privacy and security checks, documentation, approvers, and production monitoring before build accelerates.
Gate every material change
Version prompts, models, tools, retrieval, policies, and data pipelines; re-run the evidence required for the risk tier before release.
Operate and improve
Review incidents, overrides, user feedback, model or data drift, vendor changes, and control exceptions on a defined cadence.
Evaluate the system, not only the model
An enterprise outcome depends on retrieval, tools, workflow, users, and controls as much as the foundation model.
| Layer | What to test | Evidence |
|---|---|---|
| Model and prompt | Task accuracy, refusal, harmful output, robustness, language and domain performance. | Versioned test set, thresholds, failure analysis |
| Context and data | Groundedness, permission enforcement, freshness, bias, provenance, missing evidence. | Retrieval metrics, lineage, access tests, citations |
| Tools and agents | Tool choice, authority, prompt injection, excessive agency, loops, cost, recovery. | Adversarial scenarios, audit logs, circuit-breaker tests |
| Human workflow | Comprehension, override, appeal, workload, adoption, and real operating impact. | User testing, outcome metrics, incident and feedback records |
Threats that change when AI can retrieve and act
Connected agents extend the attack surface from generated text into data, tools, identities, and transactions.
AI governance and security FAQ
Can one governance process cover every AI use case?
One inventory and policy model can create consistency, but controls should scale with risk. A drafting assistant and an agent that changes supplier bank details should not face the same evidence or approval threshold.
How often should an AI system be re-evaluated?
At every material change and on a risk-based operating cadence. Model updates, prompt changes, new tools, retrieval changes, data drift, incidents, and vendor changes can all invalidate prior evidence.
Does human approval make an agent safe?
Not by itself. Approval is effective only when the reviewer sees the proposed action, evidence, uncertainty, impact, and alternatives—and has the time, authority, and interface needed to intervene.
