AI Security Assessment
Evidence-Based Evaluation of AI System Risk, Data Pipeline Security, Architecture, Governance, and Alignment to NIST AI RMF and OWASP LLM Top 10
AI systems fail differently than traditional software. The failure modes — hallucinated outputs, sensitive information exposure, bias amplification, prompt injection and jailbreaking, trust boundary violations, and training data leakage — are not addressed by conventional security controls. WAFs do not intercept adversarial prompts. Static analysis cannot find model-level risks. Standard penetration tests do not evaluate data pipelines or governance frameworks. Organizations deploying AI systems are operating with a security gap that most existing assessment methodologies cannot close.
This assessment provides a systematic, evidence-based evaluation of AI system security across six domains: model risk and behavior, training and inference data pipeline security, AI application architecture, access controls and authorization, output integrity and downstream safety, and governance and oversight. It is aligned to the NIST AI Risk Management Framework, OWASP Top 10 for LLMs (2025), and EU AI Act risk classification. Assessment combines documentation review, architecture analysis, technical interviews, and evidence-based testing — producing findings that are demonstrable, not theoretical.
The engagement is scoped to match organizational complexity — from a focused assessment of a single AI system to an enterprise-wide review of an AI portfolio. Regardless of scope, the output is a practical, prioritized action plan that security and AI engineering teams can operationalize. Governance gap analysis is included in all tiers, ensuring findings connect to policy, oversight, and accountability as well as technical controls.
Who This Is For
Ideal clients for this engagement.
The Problem
What this engagement addresses.
Model-Specific Attack Surfaces
AI systems introduce attack surfaces that do not exist in traditional software: prompt injection via user input or retrieved data, adversarial inputs designed to cause misclassification, model inversion attacks that extract training data, and membership inference attacks. These require specialized testing methodology that most security teams do not have.
Poorly Understood AI Risk
Leadership has approved AI deployments but risk has not been formally assessed, documented, or accepted. The security team lacks frameworks for categorizing AI risk, and legal and compliance teams are uncertain how existing regulatory obligations apply to AI outputs and AI-assisted decisions.
Engineering Velocity Exceeding Policy
AI capabilities are being deployed faster than governance frameworks can be established. Models are in production before data handling policies, bias assessments, or incident response procedures exist. Security reviews happen after deployment, if at all.
LLM-Specific Injection and Trust Boundary Risks
Applications built on large language models inherit LLM-specific vulnerabilities: direct and indirect prompt injection, system prompt extraction, jailbreaking, and tool misuse. These vulnerabilities operate at the semantic layer and are not detectable by traditional security controls.
Data Pipeline Exposure
Training data, fine-tuning datasets, RAG indices, and inference logs contain sensitive information and are often stored with weaker controls than production databases. Data lineage is poorly documented, making it difficult to assess what sensitive information the model has been exposed to and what it may be capable of reproducing.
Deliverables
What you receive.
AI Risk Assessment Report
Prioritized findings across all six assessment domains, each with risk rating, evidence, business impact, and specific remediation or mitigation guidance. Findings map to NIST AI RMF functions, OWASP LLM Top 10 categories, and EU AI Act risk requirements where applicable.
AI Architecture Security Review
Technical review of AI system architecture — model integration patterns, API design, access control boundaries, prompt construction, output handling, and trust assumptions. Identifies architectural issues that require design changes rather than configuration adjustments.
Data Pipeline Security Assessment
Evaluation of training and inference data pipeline security — data sourcing controls, preprocessing and annotation security, training environment access, fine-tuning data handling, RAG index access controls, and inference log data governance.
Governance Gap Analysis
Assessment of AI governance posture against NIST AI RMF and applicable regulatory requirements. Identifies gaps in AI policy, model documentation, bias and fairness assessments, human oversight mechanisms, and incident response for AI systems.
Remediation Roadmap
Sequenced remediation plan with technical controls, architecture changes, and governance improvements organized by priority. Includes effort estimates, risk reduction impact, and dependencies between items.
Methodology
How the engagement works.
Scoping & Discovery
Week 1
- AI system inventory — models in production, staging, and development; use cases and risk tier classification
- Documentation review — architecture docs, model cards, data governance policies, and prior assessments
- Regulatory and compliance requirement mapping — NIST AI RMF, EU AI Act, sector-specific requirements
- Stakeholder interviews — AI engineering, data science, security, legal, and compliance
Architecture & Data Pipeline Analysis
Weeks 1 – 2
- AI application architecture review — integration patterns, access control design, and trust boundary analysis
- Data pipeline security assessment — training, fine-tuning, and inference data flows
- RAG and retrieval system access control and data leakage evaluation
- Model access controls — API authentication, authorization, and rate limiting review
Assessment & Testing
Weeks 2 – 4
- LLM-specific vulnerability testing — prompt injection, jailbreaking, system prompt extraction, and tool misuse
- Output integrity testing — sensitive information disclosure, hallucination risk, and downstream handling
- Access control and authorization testing for AI APIs and interfaces
- Governance gap analysis against NIST AI RMF and applicable regulatory frameworks
Reporting & Roadmap
Week 4 – 5
- AI Risk Assessment Report and Architecture Security Review delivery
- Data Pipeline Security Assessment and Governance Gap Analysis delivery
- Live debrief with AI engineering and security teams
- Remediation roadmap walkthrough and prioritization discussion with stakeholders
Engagement Tiers
Scoped to your architecture.
Focused
Single AI system — one model, application, or pipeline. For organizations that need a targeted security review of a specific AI deployment before go-live or after a security incident.
- Full six-domain assessment for the in-scope AI system
- AI Risk Assessment Report and Architecture Security Review
- Data Pipeline Security Assessment for in-scope system
- Governance Gap Analysis for applicable regulatory requirements
- Remediation Roadmap
Comprehensive
Multiple AI systems — up to five models or applications across one or two business units. For organizations that need cross-system visibility and governance posture assessment.
- Everything in Focused, applied across all in-scope AI systems
- Cross-system architecture and trust relationship analysis
- Consolidated AI risk portfolio view across assessed systems
- Governance Gap Analysis with NIST AI RMF function mapping
- Integrated remediation roadmap across all systems
Enterprise
Organization-wide AI portfolio assessment including AI governance program review, policy gap analysis, and executive-level risk reporting. For enterprises with broad AI deployment and regulatory accountability.
- Everything in Comprehensive, at organizational scale
- Enterprise AI governance program assessment against NIST AI RMF and EU AI Act
- AI incident response and escalation procedure review
- Board and executive AI risk reporting framework recommendations
- AI security program maturity roadmap
- Executive briefing and CISO-level findings presentation
Prerequisites
- AI system architecture documentation and model cards where available
- Access to AI application environments for testing — production read-only or dedicated test environments
- Data governance policies, data flow diagrams, and training/inference data documentation
- Current AI policies, risk assessments, and any prior security reviews
Frequently Asked Questions
Common questions.
How is an AI Security Assessment different from a standard penetration test or application security assessment?
Standard penetration tests and application security assessments evaluate traditional vulnerability classes — injection, authentication, authorization, misconfigurations. AI systems introduce attack surfaces that require specialized methodology: prompt injection, model inversion, data pipeline exposure, and governance failures. This assessment evaluates both the AI-specific risk surface and the underlying application security posture, applying AI-specific frameworks (NIST AI RMF, OWASP LLM Top 10) alongside traditional security assessment methodology.
Can you assess AI systems built on third-party foundation models — OpenAI, Anthropic, Google, Azure OpenAI?
Yes. The assessment focuses on how your application is built on top of the model — your system prompts, retrieval pipelines, tool configurations, output handling, and access controls — not the model provider's infrastructure. The model provider's security is their responsibility; your application's security posture is yours. This is where most AI security risk lives in practice.
Do you assess non-LLM AI systems — ML models, classifiers, recommenders?
Yes. While LLM-specific testing is a growing focus area, the assessment covers traditional ML systems as well: model evasion, data poisoning, membership inference, model extraction, and adversarial input attacks against classifiers and decision systems. The governance and data pipeline domains apply to all AI system types.
How should we prepare for the EU AI Act or NIST AI RMF compliance requirements?
This assessment is designed as the natural starting point. It maps findings to NIST AI RMF functions (Govern, Map, Measure, Manage) and EU AI Act risk classification requirements, producing a governance gap analysis that identifies specific policy, documentation, and oversight gaps. The remediation roadmap sequences compliance work alongside technical security improvements so organizations can address both simultaneously.
Related Offerings
Often paired with this engagement.
Secure AI Architecture
Design and architecture engagement for building AI systems with security controls, trust boundaries, and governance mechanisms built in from the start.
AI Governance Program Build
Builds or matures an organization's AI governance program — policy framework, risk classification, oversight mechanisms, and compliance alignment to NIST AI RMF and EU AI Act.
Agentic AI Security Review
Specialized security assessment for agentic AI systems — autonomous agents that plan, use tools, and take actions with delegated authority. Addresses multi-hop instruction injection, tool authorization, and trust chain security.
Ready to discuss this engagement?
30-minute discovery call. We will discuss your application architecture, your specific concerns, and whether this assessment is the right fit.