AI Security Hub
06AI Security Pillar

Output Security Checks

Review AI-generated content before the business relies on it or sends it externally.

Business focus

Review before release

Why it matters

Security starts with a defined business boundary

Output security checks look for unsupported claims, sensitive information, policy issues, inappropriate tone, and unsafe actions before AI work is released.

Business risk

Fluent writing can still be wrong, unsupported, or inappropriate for the recipient. The consequence of an error should determine the required evidence and approval.

What this pillar covers
  • Sensitive-data redaction
  • Hallucination and factual review
  • Policy and client-commitment checks
  • Citation and source validation
Operating model

Turn review before release 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 Output Validation: What to Check Before AI-Generated Work Leaves the Business

Apply a six-part review to facts, sources, data, policy, tone, and actions.

Read the guide
Available

AI Hallucinations in Business: Reducing Confidently Wrong Answers

Separate polished language from evidence and assign review by consequence.

Read the guide
Available

AI Model Explainability: What a Business Should Be Able to Explain

Define what users, clients, leaders, and reviewers need to understand.

Read the guide

Questions for leadership

  • Which outputs can be used without review?
  • What evidence is required for material claims?
  • Who approves client-facing or high-consequence work?
  • How are corrections and recurring errors recorded?
Practical control checklist

Put the pillar into practice

  1. 1Rate the consequence of an incorrect output.
  2. 2Verify important names, dates, claims, calculations, and citations.
  3. 3Check for client, personal, credential, and confidential information.
  4. 4Confirm policy, audience, tone, and contractual commitments.
  5. 5Record approval for higher-consequence work.
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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