A CISO-grade audit of how AI expands the security exposure your organization already carries. The audit examines AI through the six domains a Chief Information Security Officer is already accountable for — governance, security operations, architecture, application security, third-party risk, and data protection — and produces a defensible exposure map, prioritized remediation plan, and executive briefing the team can present to the board, audit committee, or regulator without translation.
AI does not invent new attack surfaces from scratch. It accelerates existing ones, lowers the cost of attacks that used to require expertise, and introduces a class of vulnerability — prompt injection, model poisoning, training data exposure, agent privilege escalation — that traditional security tooling does not detect. The result is a posture that looks healthy on every existing dashboard while the actual exposure profile of the business drifts somewhere the program cannot see.
The AI IT Security Audit closes that visibility gap. It is conducted against the six core domains every CISO is responsible for, with an AI-specific lens applied to each. The output is technical clarity, exposure identification, and a prioritized remediation roadmap — sized to the organization, defensible to a board, an auditor, or a regulator.
The audit follows the operating structure of a modern CISO portfolio. Each domain receives the same treatment: assessment against current maturity, identification of AI-introduced exposure, and remediation recommendations aligned to the organization's risk appetite and budget envelope.
Where AI fits inside the broader cybersecurity strategy and risk register. The audit examines whether AI usage is captured in board-level reporting, whether AI-specific risks are reflected in the risk appetite statement, and whether the cybersecurity budget is allocated against AI exposure rather than against last year's threat model. Regulatory compliance is treated as an integrated thread — SEC cybersecurity disclosure rules, GDPR, HIPAA, state-level AI legislation, and emerging industry frameworks — not a separate workstream.
How the Security Operations Center handles AI-amplified threats and AI-native incidents. The audit reviews SOC tooling for coverage of AI-enabled phishing, deepfake-enabled social engineering, prompt injection attacks, AI-amplified ransomware targeting, and AI-driven reconnaissance. Threat intelligence sources are assessed for AI-specific coverage, and incident response playbooks are evaluated against AI scenarios the existing IR plan does not contemplate.
Whether the foundational security architecture has adapted to AI workloads and AI consumption patterns. The audit reviews Zero Trust implementation against AI-specific traffic patterns — agent-to-agent calls, model API consumption, retrieval-augmented generation pipelines, and embedding store access. Identity and Access Management is assessed across both human and non-human identities, including agent identities, service principals for AI workloads, and the proliferation of MCP server credentials. Network, endpoint, and hybrid cloud security controls are evaluated for AI-related blind spots.
How the software development lifecycle handles AI-enabled features and how MLOps practices govern internal and commercial AI models. The audit examines DevSecOps integration for AI components, evaluates SAST/DAST coverage gaps against AI behavior, reviews MLOps governance for model approval, deployment, and retirement, and assesses the organization's exposure to prompt injection, output validation gaps, and agent boundary failures.
The AI vendor risk picture, end to end. The audit inventories the organization's AI vendor footprint — foundation model providers, AI SaaS platforms, embedded AI features inside existing tools, AI-enabled libraries inside the codebase — and assesses vendor security posture, data handling practices, and contractual protections. Open-source AI dependencies are evaluated for provenance, maintenance, and known vulnerabilities.
How data flows into and out of AI systems, and whether the protections that exist for traditional data flows have been extended to AI flows. The audit examines data classification for AI training and inference data, lifecycle and retention controls for AI-related data including prompts and outputs, encryption posture across AI workloads, and data leakage prevention through generative AI tools — both sanctioned and shadow.
The AI IT Security Audit is typically sponsored by the Chief Information Security Officer, the Chief Information Officer, the Chief Risk Officer, or the General Counsel. It is most often initiated in response to a board-level question about AI exposure, an inbound enterprise customer security questionnaire that the team cannot answer cleanly, a regulatory inquiry, or a near-miss incident. It is sized for mid-market through large multinational organizations and is appropriate before — and ideally well before — an AI-driven incident, audit finding, or regulatory examination forces the work to happen on an unfavorable timeline.
Three forces are converging. SEC cybersecurity disclosure rules now require organizations to surface material cyber risk, and AI exposure increasingly qualifies. Enterprise customers are expanding security questionnaires to cover AI vendor risk. Regulators across jurisdictions are converging on evidence-based release criteria for AI systems. Organizations that establish a defensible AI security posture in the next twelve to eighteen months do so on their own timeline. Organizations that wait inherit the timeline of whoever asks the question first.
A 30-minute consultation to scope the question your leadership team needs answered. No deck, no pitch. A conversation about where your organization currently stands and what the right next step looks like.