AI Integration Audit

Validate your AI systems for security, compliance, and real-world readiness.

We provide independent, evidence-based assessment of AI assistants and LLM integrations, helping you reduce risk and meet evolving regulatory requirements.

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When is an AI Integration Audit essential?

Before deploying AI into production

AI systems behave probabilistically and can introduce unpredictable risks. An audit helps validate safety, reliability, and readiness before exposing them to users.

For regulatory and compliance readiness

With regulations such as the EU AI Act and GDPR, organisations must demonstrate control over AI risks, data handling, and decision-making processes.

When integrating AI into business-critical workflows

If AI is used in customer support, decision-making, or internal operations, failures can directly impact revenue, compliance, and reputation.

After major updates or model changes

Changes in prompts, models, or integrations can significantly affect behaviour — requiring re-validation of risks and controls.

Tailored to your AI system and business priorities

Each audit is customised based on your AI architecture, use cases, and risk profile. Depending on your goals, we assess:

AI behaviour and output quality (hallucinations, consistency, reliability)
Security risks (prompt injection, data leakage, misuse scenarios)
Data governance and privacy (handling of sensitive and personal data)
Compliance alignment (EU AI Act, GDPR, internal policies)
Bias and ethical risks (fairness, harmful outputs)
Operational readiness (monitoring, human oversight, escalation processes)
Integration risks (connections with APIs, tools, and internal systems)
Business alignment (does the AI deliver expected value and ROI?)

The scope is always focused on what matters most for your organisation — from technical risks to regulatory exposure.

Evidence-based AI audit methodology

Our approach combines technical testing and governance analysis, ensuring that all findings are traceable, measurable, and audit-ready.

1. Scoping & Audit Design

We define audit objectives, AI use cases, regulatory context, and key risks. This includes mapping your system against applicable regulations and internal policies.

2. Technical & Adversarial Testing

We conduct structured testing of AI behaviour, including:

Prompt injection and misuse scenarios
Output quality and hallucination testing
Security and data exposure validation
End-to-end AI interaction scenarios

All results are captured as evidence for further audit analysis.

3. Compliance & Governance Assessment

We evaluate findings against:

EU AI Act and GDPR requirements
Internal policies and governance standards
Risk classification and severity levels

This phase translates technical behaviour into compliance and risk insights.

4. Business & Operational Validation

We assess whether the AI system:

Aligns with business objectives
Meets expected performance and UX metrics
Has sufficient governance processes (monitoring, escalation, human-in-the-loop)

5. Reporting & Recommendations

We deliver a structured audit report with:

Clear risk profile and compliance gaps
Evidence-backed findings
Prioritised remediation plan
Readiness assessment for production use

FAQ

How is this different from a traditional IT or security audit?

AI systems are probabilistic and require behavioural testing, scenario analysis, and governance validation, which are not covered by standard audits.

Do you test the AI model itself or the integration?

We focus primarily on the integration layer and real-world behaviour, including how the AI interacts with users, data, and systems.

How long does the audit take?

Typically between 60 and 100 hours, depending on the complexity of the AI integration and scope.

Will this help with EU AI Act compliance?

Yes — the audit includes gap analysis and risk mapping aligned with EU AI Act and GDPR requirements.

Can you help implement the recommendations?

Yes — we can support with follow-up consulting, AI design improvements, and security enhancements.