AI & Security
Responsible AI claims: evidence before marketing
How to avoid unsupported AI claims by linking public statements to evidence.
Why this matters
Credible accreditation depends on consistent methods, clear decisions, and evidence that stands up to independent review. This publication translates essential expectations into practical steps so teams can prepare, communicate, and operate with confidence.
Key requirements and expectations
- Identify sensitive data and apply least-privilege access.
- Control third-party tools and integrations.
- Maintain incident response and recovery procedures.
- Prove security controls with evidence and testing.
- Claims must reference documented AI governance controls.
- Transparency about limitations is part of credibility.
- Avoid implying certification when only internal controls exist.
Evidence and records to prepare
- Security policies, access logs, and monitoring outputs.
- Risk assessments and vendor due diligence records.
- Incident response plans and tabletop exercises.
- Data retention and disposal procedures.
- AI governance policies and audit evidence.
Common pitfalls to avoid
- Unmanaged access to evidence or applicant data.
- Vendor tools without contractual security controls.
- Incident response that is untested or outdated.
- Over-collection of data without a clear purpose.
- Marketing claims without measurable evidence.
Practical checklist
- Map data flows and classify sensitive records.
- Review vendor security controls and SLAs.
- Run incident response drills and update playbooks.
- Audit access permissions on a fixed cadence.
- Approve AI-related claims through governance review.