How Do You Verify LLM RAG and AI Agent Experience When Choosing an AI Security Company?

Which expertise should an AI security company have?

An AI security company should have a team that understands application security, AI development, LLM architectures, data security, cloud infrastructure, and access control together. The core capability is combining traditional penetration testing knowledge with the behavioral and architectural risks of LLM, RAG, and AI agent systems. Looking only at model output or applying standard web tests is not enough to assess retrieval layers, tool permissions, and the trust boundaries between the model and the application.

Why does a multidisciplinary team strengthen security assessment?

In LLM applications, model security is not completely separate from application, API, data, identity, and infrastructure security. Ask how the candidate provider's security specialists work with AI developers and cloud engineers. technical evaluation criteria used when choosing an AI automation company also show why team capacity should be assessed through processes and ownership rather than by technology names alone.

• Knowledge of LLM and generative AI architectures
• Application and API security experience
• RAG and vector data layer expertise
• AI agent permission and tool-use security
• Cloud infrastructure and identity management knowledge
• Data protection and secure development experience

The enemy knows the system being used. - Claude Shannon

How should a provider's LLM security experience be verified?

A provider's LLM security experience should be verified through the architectures it has tested, scenario classes it uses, reporting method, and responsibilities held in previous projects rather than through a general claim that it “does AI security.” An LLM security company should be able to assess prompt injection, jailbreaks, sensitive information exposure, unauthorized action triggering, and application-layer trust boundaries as separate risk areas. The team should understand both model behavior and the software components surrounding the model.

Which evidence should be requested during technical discovery?

Without requiring disclosure of confidential client information, you can request a sample report structure, testing scope template, risk-rating approach, and anonymized finding examples. Understanding the application architecture of custom GPT and LLM solutions makes it easier to ask whether the security provider examines only the model or the complete combination of model, APIs, data sources, and application layers. Ask explicitly how manual verification supplements automated tooling.

• Types of LLM application architectures previously tested
• Prompt injection and jailbreak scenario coverage
• Sensitive information exposure assessment method
• Separation of automated scanning and manual verification
• Risk rating and prioritization model
• Anonymized report or finding examples

Which security tests should be applied to RAG systems?

RAG testing should examine the complete chain of document collection, chunking, embeddings, vector search, access control, and answer generation rather than only the model's final response. RAG security consulting should test whether content a user is not authorized to access can reach the model through the retrieval layer and whether knowledge sources can be influenced by untrusted or malicious content. Data separation becomes especially important in multi-user and enterprise environments.

Which retrieval and vector data controls should be examined?

Ask whether document permissions remain effective during retrieval, how data belonging to different users is separated, where indexed content originates, how updates and deletions work, and what data appears in logs. the way enterprise AI assistants work with knowledge sources demonstrates why RAG security must extend beyond the model itself to enterprise data access and integration logic.

• Document and user-level access controls
• Preservation of authorization boundaries during retrieval
• Vector database access and isolation model
• Untrusted or malicious content scenarios
• Knowledge-source update and deletion processes
• Logging and sensitive-data visibility controls

How should AI agent tools and permissions be tested?

AI agent security should be evaluated according to the tools, APIs, files, databases, and automation actions the agent can access rather than only the text it generates. An AI agent security specialist should test whether an incorrect or malicious instruction can cause the model to use a tool outside its intended authority or trigger critical actions without appropriate human control. The greater the agent's real-world execution authority, the broader the testing scope should become.

How should trust boundaries in agent architecture be verified?

For every tool, evaluate permission scope, authentication method, transaction limits, approval mechanisms, and reversibility. Understanding how AI agents and autonomous systems work shows why security testing must cover a broader execution chain than model responses alone. Agents capable of changing email, files, CRM records, payments, or enterprise systems should be reviewed for least-privilege access and critical-action approvals.

• Permission and authorization boundaries for agent tools
• Unauthorized or unexpected tool-call scenarios
• Human approval requirements for critical actions
• API key and service account security
• Transaction limits and reversibility controls
• Logging and monitoring of agent behavior

How should testing methodology and manual validation be reviewed?

An AI penetration testing company should be able to explain which threat scenarios it selects, why they are relevant, where automated testing has limitations, and when manual validation is required. Because an LLM-based system can behave differently in response to similar inputs at different times, security assessment should not be reduced to a single automated scan. Test repeatability, reproducible findings, and confirmation of real business impact are important indicators of reporting quality.

Which factors should determine the risk rating?

A finding should be rated not only on whether it can technically occur, but also on the data that becomes accessible, affected users, agent authority, attack prerequisites, and potential business impact. Ask a prompt injection testing provider whether it develops scenarios around the application's real workflows rather than relying only on generic attack lists. Remediation guidance should also identify controls that can realistically be applied to the relevant architecture.

• Threat-model-based testing scope
• Clear separation of automation and manual validation
• Application-specific adversarial scenarios
• Verification that findings are reproducible
• Combined assessment of technical and business impact
• Actionable and prioritized remediation guidance

Which results should be examined in AI security references?

For AI security references, the tested architecture, data sensitivity, integration scope, and the provider's actual security responsibilities matter more than the client name or industry alone. A comparable reference is more meaningful when it has similar data-access patterns, agent privileges, RAG structures, and operational risk levels, even if it does not use exactly the same model. If confidentiality prevents detailed disclosure, ask the provider to describe the scope in an anonymized form.

Which technical outcomes should be requested from references?

Ask which risk classes were identified, how findings were prioritized, what controls were implemented after remediation, and which results were confirmed during retesting. the management approach for security services illustrates why responsibility, follow-up, and remediation cycles matter more than a one-time list of findings. Any success claims about reference projects should ideally be explained together with their scope and measurement method.

• Architectural scope of the tested AI system
• Sensitivity level of the processed data
• Scope of RAG agent and integration components
• Risk classes identified and their priorities
• Security controls implemented after remediation
• Improvements confirmed through retesting

How should company data remain confidential during testing?

Company data confidentiality during testing should be defined through access boundaries, data minimization, secure working environments, retention periods, and secure deletion procedures. An enterprise AI security consultant should question whether production data is actually necessary and, whenever practical, prefer anonymized, masked, or controlled test data. If access to sensitive documents or customer information is required, permissions should be limited strictly to the assigned testing responsibilities.

Which technical controls should support the confidentiality agreement?

An NDA alone is not a technical security control. Ask where test data is processed, whether it is sent to external models or service providers, how sensitive information is masked in reports, how access is restricted within the testing team, and when files are deleted. If cloud services or third-party models are used, data flows should be mapped in advance and no new external transfer should be introduced without client approval.

• Minimum data access required for testing
• Anonymization and masking methods
• Role-based access boundaries within the team
• Control of external model and service data flows
• Protection of sensitive information in reports
• Retention periods and secure data deletion procedures

What should an AI security proposal clearly include?

An AI security proposal should define the components to be tested, testing method, project owners, data access boundaries, report deliverables, retesting scope, and exclusions. The more technical and measurable the proposal is, the easier it becomes to compare AI security companies by actual testing scope and responsibility rather than price alone. For assessments performed in production environments, authorization boundaries and intervention limits should be established before testing begins.

How should contract and delivery scope be clarified?

The contract should define the testing schedule, responsible people, report format, critical-finding notification method, whether source code or configuration access is required, and the right to retest. In multi-component environments such as AI-powered automation infrastructure, separating model, application, data, and infrastructure responsibilities makes the security scope easier to define accurately. If ongoing consulting is a separate service, its boundaries should also be stated.

• Model application and data components to be tested
• Scope of manual and automated testing methods
• Data access and confidentiality obligations
• Critical finding notification and escalation method
• Reporting and remediation guidance deliverables
• Retesting and ongoing consulting conditions

Who should manage remediation and the retesting process?

After the assessment, remediation should normally be implemented by the application's development and infrastructure teams, while the security provider confirms that findings were understood correctly and performs independent retesting. A clearer control model separates remediation ownership from verification instead of having the security provider make every change and then validate its own work without an independent check. When necessary, the consulting team can still support technical remediation design without taking over all implementation responsibilities.

Which final questions should complete provider selection?

Evaluate whether LLM, RAG, and agent experience can be demonstrated through real project examples, whether the methodology will be adapted to your architecture, how data confidentiality is controlled, how useful the reports are, and what retesting includes. Understanding how AI agent-based automation is built with custom software can also help determine whether the security provider genuinely understands agent architecture. When evaluating AI security providers in Ankara, technical evidence should remain more important than location alone.

• Can LLM RAG and agent experience be demonstrated?
• Are test scenarios adapted to the real architecture?
• Are data access and confidentiality conditions clear?
• Do reports explain both technical and business impact?
• Are remediation owners and consulting boundaries defined?
• Is the retesting scope stated in the agreement?