AI security assessments
AI breaks the old security model.
Model extraction, membership inference and poisoned training data sit outside the scope of a traditional security assessment. We look at the model, the data that trained it, and the pre-trained weights and libraries it was built from.
What we assess.
Model security analysis
Whether the model can be extracted through its API, evaded with crafted inputs, or broken by other adversarial attacks.
Data pipeline review
Assessment of training and inference pipelines for data integrity, leakage, and poisoning risks.
Inference attack testing
Testing for model inversion, membership inference and property inference.
Supply chain assessment
Evaluate third-party models, pre-trained weights, and ML libraries for security risks.
Compliance review
Alignment assessment against EU AI Act, NIST AI RMF, and industry-specific requirements.
Architecture guidance
Security architecture recommendations for AI/ML system design and deployment.
What the assessment covers
Model security review
Analysis of your ML model architectures for extraction, evasion and poisoning risks.
Data pipeline assessment
Evaluate training and inference data pipelines for security gaps, data leakage, and integrity issues.
Inference attack testing
Tests for model inversion, membership inference and other attacks that work through the model's outputs.
Supply chain analysis
Assess third-party models, libraries, and data sources for security and integrity risks.
Compliance alignment
Findings mapped to the AI regulations and industry standards that apply to you.
Prioritized recommendations
Remediation guidance ranked by impact, with the steps to carry it out.
How an assessment runs
Scope, architecture review and threat model first, then testing, then a prioritized report.
Scope definition
Define assessment boundaries, AI systems in scope, and specific security concerns.
Architecture review
Analyze AI/ML system architecture, data flows, and integration points.
Threat modeling
Identify potential attack vectors specific to your AI implementation.
Security testing
Conduct technical assessments including model probing and data pipeline analysis.
Risk analysis
Evaluate findings against business context and prioritize by impact.
Reporting and guidance
A written report of findings, each with a remediation recommendation.
What you get.
Executive summary
A short, non-technical account of where your AI systems stand and the main risks, written for leadership.
Technical findings
Detailed vulnerability documentation with evidence, impact analysis, and technical details.
Remediation roadmap
Prioritized action plan with implementation guidance and effort estimates.
Compliance mapping
Gap analysis against relevant AI regulations and frameworks with remediation steps.
Related services
Tell us which models you run.
In-house, fine-tuned or pulled from a public hub, we'll agree what's in scope and which attacks matter most for how you use them.