AI, Data & Intelligence
AI Security Assessments
Independent security reviews of AI systems end to end, covering data pipelines, training infrastructure, model supply chain, inference APIs and integrations, mapped against MITRE ATLAS attack techniques.
Capability overview
What ai security assessments involves
AI systems inherit every ordinary security weakness of the software around them and add new ones: poisoned training data, malicious model files, exposed notebooks with cloud credentials, inference endpoints that leak data and agents with excessive permissions. An AI security assessment examines the whole system, not only the model.
We map the architecture, identify assets and trust boundaries, and assess threats using MITRE ATLAS, the knowledge base of adversary tactics against AI systems, together with the OWASP Top 10 for LLM Applications where language models are involved. Findings are rated and paired with practical fixes.
What is included
What the assessment examines
Data pipeline security
Access to training and retrieval data, integrity controls against tampering and exposure of sensitive data in feature stores.
ML platform and infrastructure
Notebook environments, experiment trackers, GPU clusters and cloud roles checked for misconfiguration and over-permission.
Model supply chain
Provenance of pre-trained models and libraries, unsafe serialisation formats and dependency vulnerabilities.
Inference and API layer
Authentication, rate limiting, input validation and information returned by model endpoints.
Integrations and permissions
What the AI system can reach in other applications and whether that access is limited to what it needs.
How we work
How we deliver ai security assessments
Architecture walkthrough
Engineers explain the system while we build a data flow diagram with trust boundaries.
Threat modelling
Relevant ATLAS techniques and OWASP risks mapped to components, prioritising realistic attack paths.
Configuration review
Cloud, platform and pipeline settings inspected with read-only access.
Targeted testing
Selected threats tested hands-on, such as endpoint abuse or model file scanning, within agreed rules.
Findings and roadmap
Rated findings, evidence and remediation steps presented to engineering and security leads.
Related capabilities
Related capabilities in Responsible AI & AI Security
AI Model Security
Protection of machine learning models themselves against theft, tampering, poisoning, adversarial inputs and malicious model files, from training through registry to production serving.
LLM Security
Runtime security for applications built on large language models: controls against prompt injection, sensitive data disclosure, insecure output handling, excessive agency and unbounded consumption.
AI Red Teaming
Adversarial exercises in which specialists try to make your AI system misbehave, including jailbreaks, harmful or biased outputs, data leakage and misuse of tools, before real users or attackers do.
Prompt Injection Risk Assessment
A focused assessment of how exposed your RAG applications, copilots and AI agents are to direct and indirect prompt injection, and what an attacker could achieve through it.
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Where this applies
Healthcare & Life Sciences
Technology systems for regulated environments where privacy, auditability and continuity…
Manufacturing & Industrial
Connected operations, asset, field, supply chain and industrial platforms for complex operating…
Banking, Financial Services & Insurance
Technology systems for regulated environments where privacy, auditability and continuity…
E-Commerce
Digital platforms for customer experience, operations, commerce, content, marketing and service…
Questions & answers
Questions about AI Security Assessments
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionA penetration test focuses on exploiting exposed systems. An AI security assessment reviews the design, data, supply chain and platform of an AI system as well, and includes hands-on testing only where it adds evidence.
Yes. The focus shifts to how your application uses the provider: data sent, key management, output handling, permissions granted to the model and contractual protections.
A publicly available knowledge base of tactics and techniques used to attack machine learning systems, modelled on MITRE ATT&CK, and widely used to structure AI threat assessments.
It is fixed price based on the number of AI systems, components and integrations in scope, typically two to four weeks of effort per system.
Next step
Discuss ai security assessments with Acmez
Share what you need to change, build, integrate or support. We will map the practical next step.