FAQ’s

AI Assurance is the independent assessment, testing, governance and monitoring of AI systems. Its purpose is to provide evidence that an AI system is trustworthy, secure, compliant and appropriate for its intended business use.

AI testing is one part of AI Assurance.

Testing focuses primarily on whether the system performs as expected. AI Assurance also considers governance, accountability, security, data quality, human oversight, regulatory exposure, operational monitoring and business outcomes.

Traditional systems generally produce predictable outputs based on defined rules.

AI systems may produce variable outcomes and can be affected by changing data, prompts, models, users and operating conditions. Assurance therefore needs to cover areas such as hallucination, bias, explainability, drift, prompt injection and continuous monitoring.

Ideally, assurance should begin before an AI use case is approved.

Early assurance helps organisations classify risk, define accountability and identify appropriate controls before significant investment is committed. However, Inspired Assurance can also assess AI systems already in development or production.

Effective AI Assurance should enable innovation rather than obstruct it.

By defining risk, evidence and approval requirements early, organisations can reduce late-stage rework, make faster decisions and scale appropriate AI use cases with greater confidence.

Yes.

We can independently assess third-party AI products, models and providers. This may include governance, security, data handling, contractual controls, operational dependencies and vendor evidence.

Yes.

Our assurance approach can cover generative AI, large language models, RAG-based solutions and agentic workflows. The exact assessment is tailored to the purpose, risk and architecture of the AI system.

Yes.

Production assurance may include monitoring design, drift and degradation detection, incident readiness, posture assessment, control reviews and continuous evidence reporting.

Our approach can be aligned with recognised frameworks and standards including:

• NIST AI Risk Management Framework
• ISO/IEC 42001
• Gartner AI TRiSM
• OWASP guidance for LLM and agentic AI risks
• ITIL 4
• Applicable privacy and regulatory requirements

These frameworks support the work, but the engagement is tailored to the organisation’s business context and risk profile.

Depending on the engagement, evidence may include:

• AI risk and control registers
• Test strategies and results
• Governance and accountability models
• Findings and remediation priorities
• Approval-gate criteria
• Monitoring requirements
• Audit trails
• Executive and board assurance reports
• AI Assurance Packs

AI risk crosses organisational boundaries.

Relevant stakeholders may include:

• Executive sponsors
• Technology and data leaders
• Risk and compliance teams
• Information security
• Internal audit
• Legal and privacy teams
• Procurement
• Business owners
• AI and engineering teams