AI Security Hub
08AI Security Pillar

Feedback and Continuous Improvement

Keep AI controls current as models, data, tools, business processes, and risks change.

Business focus

Test and improve controls

Why it matters

Security starts with a defined business boundary

Continuous improvement turns human review, incidents, evaluation results, and business changes into updated policies, tests, and guardrails.

Business risk

An AI control can pass its launch review and become ineffective later. Models change, data shifts, integrations expand, and employees discover new uses.

What this pillar covers
  • Human-review feedback
  • Policy and workflow updates
  • Evaluation and adversarial testing
  • Guardrail and threshold tuning
Operating model

Turn test and improve controls into repeatable controls

A policy is only the starting point. For each AI use case, name the business owner, define the allowed boundary, configure the relevant technical controls, and decide what evidence proves those controls are working. Repeat the review when the model, data, connected tools, or business purpose changes.

Start with a single high-value workflow instead of trying to govern every experimental use at once. That makes it possible to test the controls with real users, find exceptions, and create a pattern the rest of the business can reuse.

StepDecisionEvidence to retain
1Scope the workflowOwner, purpose, approved data, users, and connected systems.
2Apply the controlsConfiguration, access rules, approval points, and test cases.
3Operate and reviewLogs, review results, exceptions, incidents, and change records.
Launch reading list

Start with these practical guides

Each pillar begins with one anchor guide and two supporting articles. Published guides become active automatically as they enter the blog.

Available

AI Red Teaming for Business: How to Test an AI Workflow Before It Fails

Test identity, data, retrieval, model behaviour, tools, approvals, and response.

Read the guide
Available

AI Model Governance: A Practical Lifecycle for Business

Assign decisions and evidence across design, approval, operation, and retirement.

Read the guide
Available

AI Robustness Testing: Will the System Behave Safely When Conditions Change?

Test failure behaviour across realistic, unusual, and adversarial conditions.

Read the guide

Questions for leadership

  • Which events require a policy, test, or workflow update?
  • How often are important AI use cases reevaluated?
  • Who accepts residual risk after a failed test?
  • Can the team prove that a fix remains effective after later changes?
Practical control checklist

Put the pillar into practice

  1. 1Maintain an evaluation set for each important use case.
  2. 2Record reviewer findings, incidents, exceptions, and user feedback.
  3. 3Assign owners and deadlines to failed controls.
  4. 4Add fixed failures to regression testing.
  5. 5Repeat approval when the model, data, tools, or purpose changes.
Authoritative guidance

Use recognised guidance to validate the control design

These resources help teams translate AI-specific risks into documented, testable business and technical controls. Apply them to the actual data, permissions, and actions in the workflow rather than treating them as a one-time compliance exercise.

Review a real workflow

Turn this pillar into operating controls

Map the data, access, approvals, monitoring, and evidence around one important AI use case before expanding it.

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