Know your AI attack surface before attackers do
Structured security assessment of your AI and LLM deployments — identifying prompt injection risks, data leakage vectors, model access control gaps, and OWASP LLM Top 10 exposures.
What is AI Security Assessment — Evaluate Your AI Attack Surface?
An AI security assessment is a systematic evaluation of an organisation's artificial intelligence systems — including LLM applications, AI APIs, and ML pipelines — for security vulnerabilities specific to AI. It covers the OWASP LLM Top 10, prompt injection surfaces, data exfiltration paths, model access controls, and AI-specific misconfigurations that traditional vulnerability scanners cannot detect.
Why traditional assessments miss AI-layer risk
- Traditional scanners detect web application vulnerabilities but cannot test prompt injection, jailbreaks, or LLM output manipulation
- AI systems introduce novel attack surfaces: model endpoints, context windows, retrieval pipelines, and agent tool chains
- Developers ship LLM features without security review — no standard process for AI-layer risk sign-off
- OWASP LLM Top 10 requires specialised adversarial techniques not in conventional penetration testing scope
A four-step operational model
AI Asset Discovery
Map every AI system in scope: LLM APIs, model endpoints, RAG pipelines, AI agents, fine-tuned models, and third-party AI integrations.
- LLM and model endpoint inventory
- RAG and vector database identification
- AI agent tool chain mapping
Attack Surface Analysis
Evaluate each AI surface against the OWASP LLM Top 10 — from prompt injection and insecure output handling through training data poisoning.
- OWASP LLM Top 10 mapping
- Prompt injection surface analysis
- Data leakage path identification
Adversarial Testing
Active testing of prompt injection, jailbreaks, insecure direct object references via LLM, and AI agent hijack scenarios.
- Direct and indirect prompt injection
- Jailbreak resistance testing
- Agent tool abuse scenarios
Risk-Ranked Report
Findings ranked by exploitability and business impact with specific remediation guidance and optional retest verification.
- Risk-ranked findings report
- Remediation guidance per issue
- Retest verification option
Outcomes for security teams
AI risks are invisible to standard tools
Prompt injection, model inversion, and agent hijacking require specialised testing techniques — not covered by conventional DAST or SAST scanners.
LLM features ship fast without security review
AI capabilities are often shipped in days. A structured assessment gives security teams a repeatable framework to keep pace with development.
Frameworks now require documented AI risk evaluation
EU AI Act, NIST AI RMF, and ISO 42001 all require documented AI risk assessment — this assessment provides the evidence base.
Direct answers
What is an AI security assessment?+
A structured evaluation of your AI and LLM deployments for security vulnerabilities — covering the OWASP LLM Top 10, prompt injection, data leakage, model access controls, and AI-specific misconfigurations.
How is it different from a traditional penetration test?+
Traditional pen tests focus on web, network, and application vulnerabilities. AI security assessments specifically target LLM-layer risks: prompt injection, jailbreaks, insecure output handling, and agent tool abuse.
Which AI systems are in scope?+
Any AI or LLM deployment: ChatGPT-based applications, LangChain pipelines, RAG systems, fine-tuned models, AI agents, and third-party AI API integrations.
Which compliance frameworks does it address?+
Findings are mapped to OWASP LLM Top 10, NIST AI RMF, and ISO 42001 control categories.