Reliability
Whether the AI system consistently performs the intended function under realistic operating conditions.
AI Assurance Services • Independent Assessment • AI Evaluating AI
Independent AI Assurance for Enterprise AI Systems
AegisRT helps organisations evaluate whether AI systems, LLM applications, RAG platforms and agentic workflows operate safely, reliably and as intended in practice.
AegisRT Assurance Scope
AI Assurance AssessmentSafety, reliability, robustness and governance readiness.
AI Red TeamingAdversarial testing for failure modes and behavioural weaknesses.
LLM & Agentic AI AssurancePrompt injection, data leakage, tool misuse and workflow risk.
Executive ReportingFindings translated for boards, management, risk and audit teams.
What is AI Assurance?
AI assurance is the evidence-based assessment of whether an AI system behaves as expected under realistic and adverse conditions.
AI governance defines expectations. AI assurance evaluates reality.
Why it matters now.
As AI moves from experimentation into production, organisations need more than policies. They need evidence that deployed AI systems are reliable, safe, robust and fit for purpose.
AI Governance vs AI Assurance
Both are necessary. They answer different questions.
| AI Governance | AI Assurance |
|---|---|
| Policies, standards and oversight processes. | Evidence-based evaluation of system behaviour. |
| Control design, model inventory and accountability. | Technical assessment, validation and red teaming. |
| Defines how AI should be managed. | Tests whether AI systems operate as intended. |
| Supports governance documentation. | Produces assurance findings and actionable reports. |
What AegisRT evaluates
Assessment coverage is scoped based on the AI system, deployment environment and risk profile.
Robustness
How the AI system responds to unexpected inputs, adversarial prompts, edge cases and stress conditions.
Safety
Whether system behaviour may create operational, regulatory, reputational or downstream business risk.
Governance Readiness
Whether system evidence supports oversight, risk management, audit and regulatory expectations.
How an AI Assurance assessment works
A practical workflow for enterprises, AI vendors, governance teams and advisory partners.
Scope
Define AI system purpose, architecture, deployment context and assurance objectives.
Evaluate
Assess reliability, safety, robustness and governance readiness using structured methodologies.
Challenge
Use red teaming and adversarial testing to identify weaknesses and behavioural risks.
Report
Deliver executive findings, technical observations and prioritised recommendations.
Improve
Support remediation planning, retesting and continuous assurance where required.
Why AegisRT
AegisRT combines expert assessment with proprietary autonomous testing technology.
AI Evaluating AI
Assessment supported by autonomous testing methodologies designed for AI-specific failure modes and behavioural risk.
Independent Assessment
Objective evaluation separate from implementation teams, model developers and vendors.
Governance-Oriented Reporting
Technical findings translated into practical reporting for management, risk, compliance and audit functions.
AI Assurance Framework (AIAF)
AegisRT's published framework provides an interoperable, evidence-based methodology for Independent and Continuous AI Assurance across governance context, assurance objectives, technical evaluation, professional judgement, reporting and re-assurance.
From governance expectations to assurance evidence.
AIAF complements applicable laws, standards, management systems and risk frameworks. It standardises the methodology of assurance—not the regulation of artificial intelligence.
Supporting governance, risk and audit teams.
AegisRT is designed to complement broader AI governance, audit and advisory engagements by providing the technical assurance layer.
Outputs may support board reporting, internal audit, model risk governance, responsible AI programmes and regulatory engagement.
Built for collaboration.
Advisory, certification and consulting firms can use AegisRT as a specialist technical assurance partner for AI red teaming, LLM assurance and AI system validation workstreams.
What Is AI Assurance? Why AI Governance Alone Is Not Enough
Artificial Intelligence is rapidly moving from experimentation into production. Banks are deploying AI copilots. Enterprises are integrating large language models into business workflows. Governments are exploring AI-assisted decision-support systems. Agentic AI is beginning to execute actions autonomously across systems and applications.
As AI adoption accelerates, organisations are increasingly investing in AI governance frameworks, policies and controls. However, one question remains: how do you know an AI system actually behaves as expected?
AI governance helps define how AI should be managed. AI assurance evaluates whether AI systems behave as intended in practice.
This distinction matters because an organisation may have strong governance documentation while still deploying AI systems that generate unsafe outputs, leak sensitive information, produce inaccurate responses, fail under adversarial conditions or execute unintended actions.
AI red teaming is one methodology used within AI assurance. It intentionally challenges an AI system through adversarial scenarios to identify weaknesses and failure modes before deployment.
As AI systems become embedded into critical business workflows, assurance is likely to become an essential component of responsible AI deployment.
Request an AI Assurance scope.
Share your AI use case and AegisRT will follow up to scope an appropriate assurance assessment for your AI system, LLM application, RAG platform or agentic workflow.