Your organization may already have an AI security program. You may have policies, governance controls, conventional penetration testing, and security tools in place.
But when an AI application can access sensitive data, retrieve internal information, call APIs, use external tools, or take actions on behalf of users, a conventional security assessment may not tell you the full story.
That is why choosing the right AI security assessment company matters.
The challenge is that AI security testing is still a relatively new market. Providers may use terms such as AI penetration testing, LLM security testing, AI red teaming, AI security assessment, and AI vulnerability scanning differently. Some engagements focus heavily on automated jailbreak testing, while others evaluate the entire AI application and the systems around it.
So how do you know whether a provider can actually test your AI environment?
Before you hire an AI security assessment company, ask these 12 questions!
1. What exactly will you test?
This should be the first question you ask. An AI security assessment should not automatically mean testing only the underlying model or sending prompts to a chatbot. Depending on your architecture, the assessment may need to cover:
- LLMs and model behavior
- System prompts and instructions
- AI applications and chatbots
- RAG pipelines and knowledge bases
- APIs and integrations
- Authentication and authorization
- User and tenant isolation
- AI agents and connected tools
- Memory and conversation history
- Data stores and vector databases
- MCP or other tool-connected architectures
- Cloud and application infrastructure
- Logging, monitoring, and security controls
The important question is not how many prompts a provider can run. It is whether the provider can trace an attack from the AI interface to its actual impact.
For example:
Prompt injection → manipulated retrieval → unauthorized data access → sensitive information exposure
Or:
Indirect prompt injection → AI agent manipulation → privileged API call → unauthorized action
A meaningful assessment should determine whether these attack paths are possible in your environment, not simply whether a vulnerability appears in a checklist. OWASP’s 2026 vendor evaluation criteria similarly emphasizes evaluating AI red teaming across GenAI applications, RAG systems, tool-calling agents, MCP architectures, and multi-agent workflows.
2. Do you test the AI application or only the model?
This distinction is critical. Your model may be secure in isolation while the application built around it is vulnerable. Consider an AI assistant connected to a company’s internal knowledge base. The model itself may not have a traditional software vulnerability, but an attacker could potentially manipulate retrieval, bypass authorization, access information belonging to another user, or exploit an insecure API. The real attack surface is therefore the AI system, not simply the model.
Ask the provider whether its methodology evaluates the connections between:
User → AI application → model → retrieval → data → tools → APIs → business systems
If the answer is limited to prompt and jailbreak testing, you may be buying a much narrower service than you need.
3. How much of the testing is manual?
Automation has an important role in AI security testing. It can help researchers test large numbers of inputs, identify patterns, and establish baseline coverage. But automated scanning should not be confused with a complete security assessment. AI vulnerabilities can involve context, application logic, permissions, business processes, and multi-step attack paths that require human analysis.
Ask:
- What is automated?
- What requires manual testing?
- Who reviews the results?
- How are false positives validated?
- Can your testers construct multi-step attack scenarios specific to our architecture?
A provider should be able to explain where automation improves coverage and where experienced security researchers are required to determine actual exploitability and business impact.
4. Do you test for more than prompt injection and jailbreaks?
Prompt injection is an important AI security risk, but it is only one part of the attack surface. A comprehensive assessment may need to examine:
- Direct and indirect prompt injection
- Jailbreaks
- Sensitive information disclosure
- System prompt exposure
- Insecure output handling
- RAG poisoning
- Cross-tenant data exposure
- Excessive agency
- Unauthorized tool use
- Privilege escalation
- API and integration vulnerabilities
- Authentication and authorization weaknesses
- AI supply-chain risks
- Data and model poisoning
- Memory-related vulnerabilities
- Denial-of-service and resource abuse
- Logging and monitoring gaps
The provider should explain why each test applies to your architecture, rather than simply providing a long list of AI vulnerabilities. That distinction matters because an AI security assessment should be risk- and architecture-driven.
5. Can you test RAG applications?
If your AI application uses retrieval-augmented generation, ask specifically how the provider tests the RAG pipeline. RAG introduces additional security boundaries between the model and the information it can retrieve. Testing may need to consider:
- Unauthorized document retrieval
- Cross-tenant access
- Retrieval manipulation
- Malicious content in knowledge sources
- Indirect prompt injection
- Access-control failures
- Sensitive information leakage
- Vector database security
- Insecure ingestion pipelines
A provider that says it tests “LLM security” but cannot clearly explain how it approaches RAG security may not have sufficient experience with your architecture.
6. How do you test AI agents and connected tools?
This becomes even more important when your AI system can take action. An AI agent may be able to:
- Call APIs
- Query databases
- Search internal systems
- Create or modify records
- Send messages
- Execute code
- Trigger workflows
- Access external services
At that point, the question changes from:
Can someone manipulate the AI?
to:
What can an attacker make the AI do?
Ask the provider to evaluate agent permissions, identity, authorization, tool access, execution limits, approval mechanisms, escalation paths, logging, and containment. The assessment should establish whether an attacker can move from manipulating the AI to causing a meaningful downstream impact. This is particularly important as AI systems move toward tool-calling, MCP-connected, and multi-agent architectures. OWASP’s current vendor evaluation guidance specifically includes these architectures when evaluating AI red teaming providers.
7. How do you determine what is actually exploitable?
A vulnerability finding by itself is not enough.
Ask your provider:
Can you demonstrate the real-world impact?
For example, suppose a tester identifies a prompt injection vulnerability.
The important question is whether that vulnerability can:
- Expose internal instructions
- Retrieve restricted information
- Access another customer’s data
- Trigger a privileged function
- Bypass an approval control
- Modify business data
- Reach an external system
This is where an assessment becomes useful to security and engineering teams. You need to know not just what is vulnerable, but what an attacker could actually achieve.
8. Will the assessment consider my existing application security controls?
AI security should not exist in isolation from your broader application security program. Ask whether the provider can assess the interaction between AI-specific risks and conventional security controls such as:
- Authentication
- Authorization
- API security
- Session management
- Network controls
- Secrets management
- Cloud configuration
- Application security
- Identity and access management
- Logging and monitoring
For example, an AI agent may appear to have appropriate restrictions at the model level. But if its service account has excessive permissions, an attacker may still be able to manipulate the agent into performing unauthorized actions. The strongest assessments therefore examine the complete attack path, rather than treating AI as a separate box.
9. What methodology and frameworks do you use?
Ask the provider to explain the methodology behind the engagement. You should understand:
- Which AI security risks are evaluated
- How the scope is determined
- How attack scenarios are selected
- How findings are validated
- How severity is determined
- How business impact is assessed
- How remediation is verified
- How retesting is performed
Frameworks and standards can provide useful structure, but they should not become a substitute for testing. OWASP’s 2026 vendor evaluation criteria was specifically created to help organizations distinguish meaningful AI red teaming from superficial approaches that focus only on limited attack categories. A good provider should be able to explain how the methodology is adapted to your AI architecture, not simply hand you a generic checklist.
10. What will the final report actually tell us?
Before signing an engagement, ask to see a sample report. A useful AI security report should help your security, engineering, product, and leadership teams understand:
- What was tested?
- What was found?
- How can it be exploited?
- What is the business impact?
- How severe is the issue?
- What evidence demonstrates the vulnerability?
- How should it be fixed?
- What should be retested after remediation?
Avoid reports that simply provide a list of prompts that succeeded or failed. The report should translate technical findings into actionable security decisions.
11. Can you retest after we fix the vulnerabilities?
An assessment should not end when the report is delivered. AI systems change frequently. Your organization may subsequently:
- Change the model
- Update system prompts
- Modify guardrails
- Add new tools
- Change permissions
- Update the RAG knowledge base
- Introduce new APIs
- Change authentication
- Deploy a new agent workflow
Those changes can introduce new attack paths or invalidate previous security assumptions.
Ask your provider:
What happens after remediation?
A strong engagement should include a clear retesting process and help your team understand which changes should trigger additional security testing.
12. Can you help us understand what type of assessment we actually need?
This may be the most important question of all. You may not need the same engagement as another organization.
For example:
- A customer-facing AI chatbot may require AI chatbot security testing, application security testing, API testing, and data-isolation testing.
- A RAG application handling sensitive information may require deeper testing of retrieval, authorization, data access, prompt injection, and knowledge sources.
- An AI agent connected to business systems may require agent security testing, identity and authorization testing, tool security, and adversarial attack simulation.
- An organization establishing an enterprise AI program may need a broader combination of AI security assessment, AI risk assessment, threat modeling, governance, and compliance advisory.
A provider should help you determine the right scope instead of selling you the largest possible assessment.
AI Security Assessment vs. AI Red Teaming: Which Do You Need?
These terms are often used interchangeably, but they can serve different purposes.
An AI security assessment generally provides a broader evaluation of the technical security of an AI system and its surrounding environment.
AI penetration testing focuses on identifying and validating vulnerabilities that attackers could exploit.
AI red teaming typically takes a more adversarial approach, simulating realistic attack scenarios to determine whether an AI system can be manipulated and what impact an attacker could achieve.
The right approach depends on your architecture, risk profile, maturity, and objectives.
In some environments, these approaches may be combined rather than treated as competing services.
What Should You Avoid When Choosing an AI Security Assessment Company?
There are several warning signs worth watching for.
- A provider that only talks about jailbreaks: Jailbreak testing matters, but it does not represent the entire AI attack surface.
- A provider that cannot explain its methodology: You should understand how the provider determines scope, selects tests, validates findings, and measures impact.
- A provider that relies entirely on automation: Automation can improve scale, but it should not replace human-led analysis of complex AI attack paths.
- A provider that cannot show relevant experience: Ask whether the team has actually tested AI applications similar to yours, particularly RAG systems, chatbots, agents, or tool-connected environments.
- A provider that cannot explain what happens after testing: A report is only useful if your team can use it to remediate and verify the issues.
What Does a Good AI Security Assessment Ultimately Give You?
The goal is not to receive a list of vulnerabilities. The goal is to understand how your AI system could fail under attack and what you need to change before an attacker discovers those weaknesses. A strong assessment should help you answer:
- Can an attacker manipulate the AI?
- Can they access information they should not see?
- Can they bypass authorization?
- Can they influence retrieval?
- Can they abuse connected tools?
- Can they escalate privileges?
- Can the AI take an unauthorized action?
- Can existing monitoring detect the attack?
- Can your team contain the system if something goes wrong?
Those answers are far more valuable than a simple “secure” or “not secure” rating.
How Accorian Approaches AI Security Assessments
Accorian approaches AI security testing as an evaluation of the AI system and its surrounding attack surface, rather than a standalone model or prompt-testing exercise.
Its AI security services include AI Security Assessments, AI Chatbot Penetration Testing, LLM Security Testing, Prompt Injection Testing, AI Threat Modeling, AI Red Teaming, Agentic AI Security Assessments, and Third-Party AI Security Validation.
The approach can extend across AI applications, models, RAG environments, APIs, integrations, agents, connected tools, and supporting infrastructure, depending on the scope and architecture.
Accorian has also published real-world AI security assessment work involving an AI chatbot where the assessment focused on vulnerabilities that could lead to issues such as sensitive information exposure and unauthorized access.
The objective is straightforward: identify what an attacker could actually do, demonstrate the impact, and give the organization a practical path to remediation.
Not Sure Which AI Security Assessment Your Organization Needs?
You do not need to start by choosing between AI penetration testing, AI red teaming, LLM security testing, or an AI security assessment.
Start with your environment.
- What AI systems are you deploying?
- What data can they access?
- Which users can interact with them?
- What tools and APIs can they call?
- Can they take actions autonomously?
- What happens if an attacker successfully manipulates them?
Those answers can determine what should be tested, how deeply it should be tested, and which assessment approach makes sense.
If you are evaluating AI security providers, Accorian can help you define the right assessment scope based on your AI architecture, attack surface, data exposure, and security objectives.
