AI Governance & NIST AI RMF Alignment

Artificial intelligence systems increasingly shape how organisations are described, interpreted and recommended. Large language models and generative search systems influence reputation, visibility, credibility and decision-making at scale.

AwarenessAI operates directly within this environment.

Because our methodology evaluates and interprets AI-generated outputs, governance is not optional. It is foundational. AI governance matters for AwarenessAI for three primary reasons:

1. We rely on third-party AI systems — Our methodology uses outputs from large language models and generative search tools. These systems evolve rapidly, may produce inconsistent outputs, and operate outside our direct control.
2. We assess representation risk — Our assessments influence how organisations interpret AI-generated narratives about their brand. Poor governance could amplify misinterpretation or introduce bias.
3. We advise on AI-related risk exposure — Clients use our findings to inform strategic decisions around digital presence, structured data, trust signals and content accuracy.

AI representation measurement introduces specific risks including model hallucination, output variability, implicit bias within training data, overinterpretation of probabilistic outputs, false confidence in scoring systems, changes in model behaviour without notice, and conflation of visibility optimisation with behavioural manipulation.

If we are advising others on AI representation risk, we must demonstrate that our own processes are governed responsibly.

The NIST AI Risk Management Framework (AI RMF) provides a structured, internationally recognised approach to identifying, measuring and managing AI-related risks through four core functions:

• Govern — Establish oversight, accountability and internal controls
• Map — Understand context, risks and system boundaries
• Measure — Evaluate outputs and identify risk indicators
• Manage — Respond to identified risks and improve controls

These functions operate continuously and iteratively. AwarenessAI has voluntarily aligned its AI Recommendation Optimisation (AIRO) methodology with this framework to position AI representation measurement as a governance-aware discipline rather than a purely marketing-oriented activity.

How We Use AI Systems

AwarenessAI does not develop or train proprietary AI models. We rely exclusively on third-party generative AI systems accessed via official APIs, including Google Gemini, Grok, Claude, Meta AI, ChatGPT and Perplexity. These systems are selected for comparative visibility across varying architectures, training data sources and retrieval mechanisms.

Our methodology combines automated processing (prompt execution, output capture, initial scoring logic) with human oversight (prompt design, interpretation of ambiguous outputs, contextual accuracy assessment, final reporting and quality assurance).

Final conclusions and strategic recommendations are always subject to human review. We do not rely on fully autonomous AI-driven decision-making when advising clients.

Govern

Accountability sits with a defined Founder & AI Governance Owner responsible for model selection oversight, methodology approval, prompt design review, monitoring third-party system changes, governance documentation and escalation of AI risk concerns.

Our internal AI use principles state that:

• AI outputs are probabilistic and non-deterministic
• Human review is required before final reporting
• AI systems are used for evaluation, not manipulation
• No client data is used for model training
• No attempt is made to influence underlying model architectures

We commit to annual governance review, interim review when significant AI system updates occur, and model version logging where available.

Map

AIRO is designed to measure how organisations are described across leading generative AI systems, identify inconsistencies and omissions, highlight representation risks, and provide structured remediation guidance. It is not designed to manipulate model architectures, circumvent AI safeguards, guarantee ranking position, or override probabilistic behaviour.

We have identified seven primary risk categories in AI representation measurement:

1. Factual Inaccuracy — Models may produce incorrect statements
2. Omission — Important information may be excluded
3. Bias — Outputs may reflect implicit training data biases
4. Variability — Outputs may differ across sessions or time periods
5. Overinterpretation — Deterministic meaning attributed to probabilistic outputs
6. Model Drift — Underlying behaviour may change without notice
7. Scoring Abstraction — Scoring may oversimplify complex narrative representations

Stakeholders potentially affected include client organisations, marketing and communications teams, risk and compliance teams, end users relying on AI-generated summaries, and broader public audiences.

Measure

Measurement uses structured prompt frameworks at two tiers: a Snapshot Assessment with fixed prompts evaluating core accuracy, consistency and visibility, and an Advanced Audit with expanded and custom prompts tailored to sector and risk exposure.

Testing is conducted across multiple AI systems independently. A composite GEO Score is generated based on accuracy indicators, consistency measures, representation completeness and identified risk signals. Findings are classified as High, Medium or Low Risk.

The GEO Score is an interpretive metric, not a deterministic ranking guarantee. All assessments include timestamp recording for traceability and are subject to human review before client-facing reporting.

Manage

When risks are identified, AwarenessAI provides structured risk categorisation, contextual explanation, impact clarification and remediation recommendations. Remediation guidance may include structured data improvements, content clarification, metadata optimisation and trust signal strengthening.

Retesting is available under structured maintenance arrangements. Where we become aware of material changes in model behaviour affecting previously assessed outputs, clients may be notified accordingly. If significant inaccuracies are identified during assessment, findings are documented internally, human review is conducted and risk classification is reassessed.

Insights from repeated assessments feed back into refinement of prompt frameworks, scoring logic and risk categories.

AIRO does not guarantee:

• Permanent changes in AI system outputs
• Control over generative AI ranking behaviour
• Deterministic or fixed model responses
• Regulatory compliance certification
• Elimination of all representation inaccuracies
• Immunity from future model drift

AwarenessAI does not directly influence, retrain or manipulate third-party AI systems. Our methodology does not modify model architecture, alter training data, bypass AI safeguards, or attempt to exploit system vulnerabilities. We provide advisory guidance focused on improving publicly available digital clarity and consistency.

AIRO provides structured analytical assurance at the level of comparative representation analysis, risk categorisation, contextualised interpretation and human-reviewed remediation guidance. It does not provide legal guarantees, regulatory certification or deterministic prediction of future AI behaviour.

Our role is evaluative and advisory. Responsibility for underlying model behaviour remains with model providers.

AI governance is iterative. Planned maturity enhancements include:

• Formal AI risk register implementation
• AIRO methodology versioning framework
• Expanded model version logging
• Structured bias sensitivity review
• Independent advisory review of methodology
• Annual governance review publication

Governance alignment is treated as a continuous improvement process rather than a static compliance exercise.