AI Security Assessment

What Is an AI Security Assessment?

An AI Security Assessment is a specialised security review designed for systems that use artificial intelligence, machine learning, large language models, generative AI, retrieval-augmented generation, copilots, or autonomous agents.

AI systems introduce risks that are not fully addressed by a conventional application penetration test. Their behaviour can be probabilistic, their attack surface can change according to prompts and retrieved content, and security failures may originate from the model, data, agent permissions, integrations, infrastructure, or governance model.

Why Do You Need an AI Security Assessment?

1. Identify Prompt-Injection Risks: Attackers may manipulate direct prompts, external content, retrieved documents, or tool responses to influence model behaviour.
2. Test Jailbreak Resistance: Guardrails may be bypassed through role manipulation, encoded instructions, multi-step prompts, or contextual attacks.
3. Prevent Sensitive Data Leakage: AI systems may expose confidential information through prompts, responses, training data, logs, embeddings, or connected data sources.
4. Control Agent Permissions: Autonomous agents may be able to access systems, send messages, modify data, execute transactions, or perform actions beyond their intended purpose.
5. Secure AI Integrations: Plugins, APIs, tools, external models, and third-party components may create new attack paths and supply-chain risks.
6. Protect Models and Data: AI assets may be targeted through model theft, poisoning, data manipulation, extraction, or unauthorised access.
7. Strengthen AI Infrastructure: Cloud misconfigurations, insecure APIs, excessive identities, exposed endpoints, and weak platform controls can undermine the entire AI environment.
8. Support AI Governance: Organisations need documented ownership, risk controls, policies, review processes, monitoring, and evidence for customers, auditors, and management.
9. Prepare for Enterprise Adoption: Security weaknesses may delay customer approval, procurement, production deployment, or the use of regulated information.
10. Validate Existing Controls: An independent assessment determines whether AI guardrails, filters, permissions, monitoring, and governance controls work under adversarial conditions.

When Should You Conduct an AI Security Assessment?

1. Before Launching an AI System: Assess customer-facing or internal AI before production deployment.
2. Before Connecting AI to Sensitive Data: Review security before allowing access to financial, health, personal, confidential, or regulated information.
3. Before Enabling Agentic Actions: Test permissions and safeguards before an agent can send emails, modify records, execute code, approve requests, or perform transactions.
4. When Deploying a Copilot: Assess assistants connected to internal applications, files, databases, ticketing platforms, or collaboration tools.
5. When Building an AI-Enabled SaaS Platform: Validate the security posture before enterprise customers begin conducting their own security reviews.
6. After Major Model or Architecture Changes: Reassess after changing models, prompts, retrieval sources, tools, APIs, integrations, infrastructure, or guardrails.
7. Following an AI Security Incident: Investigate the immediate issue and identify related weaknesses across the wider AI estate.
8. When Establishing AI Governance: Use technical findings to support policies, risk registers, control frameworks, and management oversight.
9. Before a Customer or Regulatory Review: Prepare evidence that AI security risks have been identified, tested, prioritised, and addressed.
10. When Moving from Experimentation to Production: Security requirements should mature when AI begins supporting real business processes.

What We Offer

XDefense assesses the existing environment and develops a target security architecture aligned with the organisation’s technology strategy, business priorities, risk profile, and compliance requirements.