AI Security Assessment
An independent technical evaluation of the security of a Generative AI system — yours, in testing, or proposed by a vendor — before you scale it, sign off on it, or put it through an audit. It's the same vetting we apply to our own projects, available for systems we didn't design too.
You already have an AI system in production or testing
And want to know whether it is genuinely secure before scaling it to more users or processes.
A vendor has proposed an AI system to you
And you need to evaluate its security independently before signing.
You need to answer an internal or regulatory audit
On a system that uses generative models, and need an honest technical snapshot of where it stands.
You are assessing your AI Act readiness
And prefer an honest evaluation over a checklist that has been ticked off in advance.
What we analyze
Data governance
Classification of the data processed, minimization, no training on client data without explicit authorization, segregation between environments and clients.
On-premise, cloud & hybrid
Deployment follows the organization’s constraints, not the other way around: when data residency is non-negotiable, the architecture stays on-premise or hybrid.
Audit trail
Every source consulted, action taken and assisted decision is logged in a verifiable way, with reference to the originating data.
Human-in-the-loop
High-impact actions remain subject to explicit human confirmation; the system flags uncertain cases instead of deciding on behalf of people.
Prompt injection defense
Every piece of content retrieved from documents, emails or external pages is treated as untrusted input: system-prompt isolation, sanitization, control over the actions agents can execute.
AI Act
Risk classification of the system, technical documentation and transparency requirements aligned with the European regulatory framework, handled from the design phase onward.
Roles & responsibilities
Explicit definition of who can configure, supervise, approve or disable the system, and who is accountable for each stage of its operation.
Logging & traceability
Technical and application logs retained according to agreed policies, usable for internal audits, external reviews and incident analysis.
How we verify it
- 01
Threat modeling
We identify the abuse scenarios most relevant to that specific process: exposed data, unauthorized actions, prompt injection.
- 02
Red teaming
Targeted attempts to bypass guardrails, manipulate the system prompt, or obtain data and actions that were never intended.
- 03
Testing on real cases
Verifying accuracy and behavior on real data and scenarios, not just on prepared examples.
- 04
Permission review
Cross-checking roles, access and data segregation before releasing to production.
What you receive
Threat map
The most relevant abuse scenarios for that specific system: exposed data, unauthorized actions, prompt injection.
Red teaming results
Targeted attempts to bypass guardrails or obtain unintended data or actions, with the vulnerabilities found.
Permissions and roles review
Who can configure, supervise or disable the system, and whether data segregation matches the intended use.
Regulatory alignment status
Where the system stands against the AI Act, GDPR and NIS2, and what's missing to be ready.
It's not a formal certification audit (ISO 27001, SOC 2): it's an independent technical evaluation, useful both as a first step toward certification and as a check before an external audit or a signature.
We don't publish a price list for the AI Security Assessment: the scope depends on the system being analyzed — data volume, integrations, risk level. We tell you after seeing the system, not before.
Show us the system, not just the spec sheet.
An AI Security Assessment starts from a real system — yours or a vendor's — not a generic security questionnaire.
Request an AI Security Assessment