Generative AI Security Testing
Generative AI can improve productivity, support customer service, automate research, and help employees work with information more efficiently. However, introducing a large language model or AI-enabled application also creates security risks that traditional application testing may not fully identify.
An AI system can be manipulated through carefully constructed prompts, exposed data sources, insecure integrations, excessive permissions, or weaknesses in the surrounding application. Sensitive information may appear in generated responses, safety controls may be bypassed, and connected tools may perform actions that were never intended.
Generative AI Security Testing from CA helps organisations assess how AI applications behave when faced with misuse, manipulation, and realistic attack scenarios. We examine the complete solution, including the model, prompts, data, retrieval components, access controls, integrations, and supporting infrastructure.
Why This Matters
Generative AI systems operate differently from traditional software. Their responses are influenced by natural-language instructions, previous conversation content, connected information sources, system prompts, model configuration, and the tools available to the application.
This creates risks that may not be visible during normal functional testing. A system can appear to work correctly for legitimate users while still revealing restricted information, following malicious instructions hidden in external content, or allowing someone to bypass its intended limitations.
Effective Generative AI security testing helps you:
Protect sensitive information
Identify situations in which confidential data, personal information, system instructions, credentials, or restricted source material could appear in an AI-generated response.
Test resistance to manipulation
Assess whether direct and indirect prompt injection, jailbreak techniques, encoded instructions, or multi-step conversations can bypass established safeguards.
Secure connected tools and data sources
Examine whether the AI system can access information or perform actions beyond the permissions and purpose approved by the organisation.
Support safer deployment
Discover weaknesses before an AI application is made available to employees, customers, partners, or the wider public.
Our Approach
We do not assess only the underlying language model. The security of a Generative AI solution depends on the complete environment in which the model operates.
Our approach covers the user interface, application logic, system prompts, retrieval process, data sources, identity controls, APIs, plugins, connected tools, model configuration, logging, monitoring, and supporting cloud infrastructure.
Testing is adapted to the intended purpose of the system and the possible consequences of misuse. An internal drafting assistant requires a different level of assessment from an AI agent that can access customer records, approve transactions, change data, or perform actions in connected systems.
Understand the AI solution and define the testing scope
We begin by understanding what the Generative AI system is designed to do, who will use it, which information it can access, and what actions it is allowed to perform.
We identify the model, application, prompts, retrieval components, data repositories, APIs, plugins, identity services, external providers, and connected business systems included in the solution.
We also review user roles, expected behaviour, restricted activities, existing safeguards, logging arrangements, and known limitations. Particular attention is given to sensitive information and high-impact functions.
This creates a clear testing scope and helps us build realistic scenarios based on how the system will actually be used.
Test prompts, safeguards, and information exposure
We assess whether users can manipulate the system into ignoring instructions, changing its role, revealing protected information, or producing content outside its approved purpose.
Testing may include direct prompt injection, jailbreak attempts, instruction conflicts, conversation manipulation, encoded requests, multilingual inputs, unusual formatting, and repeated attempts designed to weaken safeguards gradually.
We also examine whether system prompts, internal instructions, confidential information, retrieved documents, personal data, or details belonging to another user can be exposed through generated responses.
The objective is not simply to produce an unexpected answer. We determine whether the behaviour creates a genuine security, privacy, operational, or compliance risk for the organisation.
Assess integrations, retrieval, and agent actions
Generative AI applications often connect to documents, databases, websites, APIs, plugins, and business systems. These integrations can provide valuable capabilities, but they also expand the attack surface.
We test whether malicious instructions hidden in retrieved documents, emails, websites, or other external content can influence the model. This is particularly important for Retrieval-Augmented Generation solutions and agents that process information from sources the organisation does not fully control.
We also assess whether the system respects user permissions when retrieving information. A user should not receive a restricted document simply because the AI application can access the underlying repository.
Where the system can perform actions, we test whether a manipulated instruction could trigger unauthorised changes, disclose information, contact an external service, or misuse a connected tool. Approval steps, action limits, and human oversight are reviewed as part of the assessment.
Prioritise findings and strengthen ongoing protection
We document confirmed weaknesses in clear technical and business language. Each finding explains the affected component, the test conditions, the possible consequences, and the recommended corrective action.
Findings are prioritised according to exploitability, required access, data sensitivity, level of automation, potential business impact, and the effectiveness of existing safeguards.
Recommendations may include strengthening system prompts, improving access controls, separating data, restricting tools, filtering retrieved content, introducing additional validation, limiting automated actions, improving logging, or adding human approval for sensitive decisions.
After corrective work has been completed, CA can retest the relevant functions. We can also help establish regular testing and monitoring because models, data sources, integrations, prompts, and supplier services may change after deployment.
What Sets CA Apart
Testing of the complete AI solution
We assess the model together with prompts, applications, data sources, retrieval components, identities, APIs, plugins, tools, and infrastructure.
Realistic adversarial scenarios
Testing reflects how employees, external users, attackers, and malicious content could attempt to manipulate the system in practice.
Business-focused risk assessment
We explain how each weakness could affect information, customers, operations, compliance obligations, finances, and organisational trust.
Practical remediation support
Our recommendations help development, security, privacy, cloud, and business teams address weaknesses without unnecessarily limiting the value of the AI solution.
The Business Value
Generative AI Security Testing from CA gives your organisation greater confidence that an AI application can be introduced without exposing unacceptable security and privacy risks.
Testing identifies weaknesses that standard vulnerability scanning and functional testing may overlook. This includes prompt manipulation, information leakage, insecure retrieval, excessive permissions, unsafe integrations, and AI agents performing actions outside their intended boundaries.
Development and technical teams receive clear recommendations that can be applied before wider deployment. Addressing problems earlier is usually less disruptive and less costly than correcting them after employees, customers, or third parties have begun relying on the system.
Independent testing also supports internal approval, customer assurance, risk assessments, compliance reviews, and responsible AI governance. It provides evidence that the organisation has considered how the system could be misused, not only how it should behave during normal use.
The result is a better-protected Generative AI environment with stronger safeguards, clearer limitations, more reliable monitoring, and greater confidence in the responsible use of AI.
Next Step
A Generative AI application should be tested not only for what it is expected to do, but also for how it might respond when users, external content, or connected systems attempt to manipulate it.
Contact CA for an initial consultation. We will help you define the right testing scope, assess your Generative AI solution against realistic attack scenarios, and develop a practical plan for addressing the identified risks.