Security Testing for Generative and Agentic Artificial Intelligence

Instead of using a standardized testing checklist, ScienceSoft first examines your AI environment to determine what needs security testing and why. We look at the GenAI and agentic AI solutions to be tested, their role in business workflows, user roles, data types, deployment model, integrations, actions agents can take autonomously or with approval, and relevant industry and regulatory risks. We also identify the sensitive data the solutions may process, such as PHI, PII, or financial records. This gives us the context needed to define a testing scope that reflects how your AI is actually used.

ScienceSoft is ready to sign a non-disclosure agreement (NDA) or its equivalent before any project discussions to protect confidential information.

ScienceSoft maps the AI attack surface to understand where the solution can receive instructions and data, access resources, connect to other systems, and trigger actions.

We review:

• AI orchestration and retrieval: prompt orchestration, RAG pipelines, vector databases, and memory.
• Tools and integrations: APIs, plugins, MCP servers, connected systems, and third-party services.
• Identity and access controls: how identities, roles, and permissions are managed and enforced.
• Logging and traceability: how AI interactions, retrieval events, tool calls, and agent actions are recorded.
• Data and execution flows: how data moves through the solution and how AI actions are triggered and executed.

The review also traces sensitive data across prompts and retrieved context, logs and memory, model provider environments, and downstream systems to identify where it enters, persists, and leaves the AI solution.

ScienceSoft documents the attack surface, data flows, and security-relevant components and exposure points. These findings become the basis for threat modeling, where we identify realistic attack, misuse, and control-failure scenarios.

Based on the mapped attack surface, ScienceSoft identifies what could realistically go wrong and how. This includes deliberate attacks, misuse of legitimate AI capabilities, and failures of safeguards such as access controls, anonymization, or approval gates.

For example, healthcare AI testing may include attempts to retrieve another patient’s records through a chatbot, expose excessive PHI through RAG results, or bypass data anonymization rules.

ScienceSoft prioritizes the scenarios by risk and translates them into a focused security test plan.

ScienceSoft examines the security of the AI solution across its behavior, data access, permissions, integrations, application components, and supporting infrastructure.

Our team combines automated scanning and prompt fuzzing with expert review and hands-on testing. Some weaknesses can be identified directly in configurations, permissions, data handling, logging, or dependencies; others require adversarial testing to verify how the AI behaves under manipulation or whether a security boundary can be crossed.

The result is evidence-backed findings showing which security gaps are present and, where relevant, how they can affect the AI solution in practice.

ScienceSoft prepares a security testing report that documents the completed tests and confirmed vulnerabilities. For each finding, the report provides the details needed to reproduce the issue, explains its potential impact and likelihood of exploitation, and recommends remediation measures.

Upon request, ScienceSoft can help the client’s team implement the recommended fixes. After remediation, ScienceSoft retests the affected areas to verify the fixes and confirm they didn't create new vulnerabilities.