What does an AI security assessment cover?

Typical scope includes data flows, identity and access, model and supplier risk, RAG permissions, prompt injection, tool or agent permissions, logging, testing, incident response and change management.

Is a penetration test enough for AI security?

No. Traditional security testing remains important, but AI systems also require review of model behaviour, retrieval permissions, prompt injection, agent actions, data use and lifecycle changes.

AI Security Assessment Europe | RAG, Agents & AI Vendors

What this service is for

For organisations evaluating an AI assistant, RAG system, copilot, agent, vendor platform or AI-enabled workflow before deployment, renewal or wider rollout.

ArchitectureTrace data, identity, retrieval, model context, tools and output paths.

MisuseTest realistic failure modes such as prompt injection, leakage and excessive agency.

OperationsEnsure logging, incident response, change control and fallback are defined.

Assessment areas

Area	Key questions	Example evidence
Data	What enters the system, where is it processed, retained or reused?	Data-flow map, retention settings, supplier terms.
Identity & access	Who can query, administer, connect sources and invoke tools?	SSO/RBAC design, privileged access, tenant controls.
RAG	Are source permissions preserved and embeddings protected?	Retrieval architecture, ACL filtering, deletion flow.
Prompt injection	Can users or documents override policy or trigger unsafe behaviour?	Adversarial test cases and mitigations.
Agents & tools	Can the model take actions beyond the user’s intended authority?	Tool allowlists, scopes, approval gates.
Vendor / model	How do model changes, subprocessors and customer-data terms affect risk?	Vendor assessment and change-notification controls.
Logging	Can material actions and incidents be reconstructed?	Audit events, SIEM integration, retention.
Human oversight	Where must a person verify, approve, intervene or stop the workflow?	Escalation and fallback design.

Typical deliverables

Architecture and trust-boundary review.
Threat and misuse scenario register.
RAG and source-permission assessment where relevant.
Agent/tool permission and approval review.
AI vendor and model-risk findings.
Logging, monitoring and incident-readiness gaps.
Prioritised remediation plan with residual risk.

Security references used

The assessment can draw on OWASP GenAI guidance, the NIST AI Risk Management Framework and its Generative AI Profile, and ISO/IEC 27001 security-management principles. These references are used as practical baselines; they do not replace a system-specific threat model or testing.

When to assess

Moment	Why it matters
Before procurement	Contract, data and architecture constraints can still be changed.
Before pilot	Boundaries and test cases can be designed before users depend on the system.
Before production	Logging, incident response and human oversight should be operational.
After major model / tool changes	Behaviour and attack surface may have materially changed.

Assessment boundary: an AI security assessment reduces uncertainty; it cannot guarantee that a system is secure or eliminate all model behaviour risk. Findings should be managed through ongoing monitoring and change control.

Reference sources

Need a focused review of an AI system or vendor?

SundAI can assess the control surface and turn findings into a prioritised remediation plan.

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Review the AI system before it becomes production infrastructure

Assessment areas

Typical deliverables

Security references used

When to assess

Reference sources

Need a focused review of an AI system or vendor?