AI has moved beyond experimentation. Enterprises are deploying LLMs, copilots, RAG applications, AI chatbots, and autonomous agents that can access internal data, call APIs, update records, and trigger business workflows.
That changes the security question.
Your AI may be producing the right answers. But what happens when someone deliberately tries to make it do the wrong thing?
Accorian’s AI security assessments are designed around that question. We test AI systems as security systems, not simply as AI products, examining the model, application, data, integrations, APIs, identities, tools, and connected infrastructure.
The urgency is increasing. Check Point’s 2026 AI Security Report found that organizations were using an average of 10 AI applications each month, while high-risk GenAI prompts involving sensitive corporate, personal, or regulated data doubled from 2% to 4% over the year. Between 87% and 93% of organizations recorded at least one high-risk GenAI interaction every month.
Meanwhile, longer malicious prompt-injection payloads increased roughly fivefold between March and May 2026.
The question is no longer whether your organization is using AI.
It is whether your security controls can withstand an attacker targeting it.
What Is an AI Security Assessment?
An AI security assessment is a technical evaluation of whether an AI system can be attacked, manipulated, exploited, or abused, and what an attacker could achieve if they succeed.
Unlike a conventional application security assessment, AI security testing must account for risks created by the model itself and the way the model interacts with data, users, applications, APIs, tools, and other systems. Depending on your architecture, this can include:
- LLM and model behavior
- AI applications and chatbots
- RAG pipelines and vector databases
- System prompts and instructions
- APIs and integrations
- Authentication and authorization
- AI agents and connected tools
- Data sources and knowledge bases
- MCP and other tool-connected architectures
- Cloud and application infrastructure
- Third-party AI services and models
NIST’s Generative AI Profile recognizes that AI risks can differ from traditional software risks and can emerge throughout the AI lifecycle, from design and development through deployment, operation, and decommissioning.
That means testing only the model is not enough.
You need to test the attack path.
For example:
Prompt injection → manipulated retrieval → unauthorized data access → sensitive information exposure
Or:
Indirect prompt injection → AI agent manipulation → privileged API call → unauthorized business action
The real security question is not simply “Can the AI be tricked?”
It is:
“If the AI is tricked, what can an attacker make it do?”
10 Tests Every Enterprise Should Run Before Trusting AI
1. Can an attacker bypass the AI’s instructions?
Prompt injection remains one of the most important AI security tests. An attacker may attempt to override system instructions, change the model’s behavior, extract restricted information, or manipulate how the application processes subsequent instructions.
OWASP continues to identify prompt injection as a core LLM security risk, while its 2025 guidance also highlights system prompt leakage and excessive agency as significant risks in modern AI applications.
Testing should go beyond obvious prompts and examine direct and indirect injection paths through documents, emails, web pages, knowledge bases, uploaded files, and retrieved content. This matters particularly for RAG and agentic applications, where untrusted content can become an instruction the AI acts upon.
2. Can the AI expose sensitive information?
AI systems frequently have access to information that traditional applications do not expose through a single interface. That could include customer records, internal documents, source code, credentials, financial information, healthcare data, proprietary prompts, or regulated information.
Check Point found that high-risk prompts involving sensitive information doubled from 2% to 4% over the previous year. A meaningful assessment should test whether an attacker can:
- Extract sensitive data through prompts
- Access information belonging to another user
- Retrieve restricted documents
- Recover system instructions
- Access data through RAG
- Bypass data classification controls
- Extract information through model responses
The issue is not just whether the AI has access to sensitive data. It is whether the wrong person can make it reveal that data.
3. Can users cross authorization or tenant boundaries?
AI applications often sit on top of complex permission models. A chatbot may serve multiple customers. A copilot may operate using an employee’s identity. An agent may have access to internal systems. A RAG application may retrieve information from multiple repositories.
That creates a critical question:
Can one user’s AI interaction retrieve another user’s data?
Testing should examine authentication, authorization, role-based access, tenant isolation, session handling, API permissions, retrieval controls, and downstream system access.
A conventional application test may confirm that an API requires authentication. AI security testing must go further and determine whether the AI can be manipulated into using that authenticated access incorrectly.
4. Can an AI agent take actions it should not?
This is where agentic AI fundamentally changes the threat model. An AI agent may be able to send emails, access databases, call APIs, modify records, execute workflows, or interact with external tools.
OWASP identifies excessive agency as a major risk when AI systems have excessive functionality, permissions, or autonomy. The security assessment should therefore ask:
- What tools can the agent access?
- What permissions does its identity have?
- Can it execute actions without human approval?
- Can an attacker influence which tool it calls?
- Can it chain multiple tools together?
- Can it escalate privileges?
- Can it perform actions outside its intended workflow?
This is no longer hypothetical. OWASP’s 2026 exploit reporting includes cases involving agentic systems, tool misuse, unexpected code execution, identity and privilege abuse, and rogue-agent behavior.
Treat an AI agent as a security identity, not simply another software feature.
5. Can RAG and knowledge bases be manipulated?
RAG systems introduce another attack surface because the AI does not rely only on its underlying model. It retrieves information from external sources and uses that information to generate responses.
An attacker may attempt to manipulate those sources so that the AI retrieves malicious instructions or unauthorized information. Testing should examine:
- Document poisoning
- Retrieval manipulation
- Unauthorized document access
- Cross-tenant retrieval
- Malicious embedded instructions
- Vector database security
- Metadata manipulation
- Access-control failures
- Data segregation
NIST’s GenAI Profile emphasizes that AI risks can exist at the model, application, and broader ecosystem levels.
If your AI can retrieve information, that retrieval path needs to be tested as part of the security assessment.
6. Can the AI’s APIs and integrations be abused?
AI rarely operates alone. Modern AI applications connect to APIs, databases, SaaS platforms, identity providers, cloud services, plugins, MCP servers, internal applications, and third-party models.
Every connection expands the attack surface. Testing should determine whether an attacker can manipulate the AI into:
- Calling unauthorized APIs
- Accessing privileged endpoints
- Executing unintended functions
- Bypassing application controls
- Extracting credentials or tokens
- Accessing systems outside the intended scope
Netskope’s 2026 research highlights the growing security implications of agentic AI and MCP as organizations connect AI systems to internal data stores and external models.
The more connected your AI becomes, the more important it is to test the systems around the model, not just the model itself.
7. Can jailbreaks bypass your AI’s safeguards?
Guardrails are not security controls until they have been tested against adversarial behavior. Jailbreak testing attempts to bypass restrictions designed to prevent harmful, unauthorized, or unintended outputs and actions.
But a mature assessment should not stop at demonstrating that a jailbreak works.
The critical question is:
What does the jailbreak enable?
If a jailbreak only produces an unexpected response, the business impact may be limited. If it allows the attacker to retrieve sensitive information, manipulate a workflow, invoke an unauthorized tool, or access a privileged system, the risk is significantly greater.
Accorian’s testing of more than 100 production-grade AI chatbots found 49% susceptible to jailbreak bypass. The same assessment found prompt injection exposure in 82%, internal instruction exposure in 61%, and PII exposure in 35% of the tested sample. These figures represent Accorian’s assessed sample and should not be interpreted as universal prevalence across AI applications.
8. Can the AI supply chain introduce a vulnerability?
Your organization may not build the model it uses. You may rely on a foundation model provider, AI API, open-source model, RAG component, plugin, embedding model, data provider, MCP server, or third-party AI application.
That creates supply-chain risk. A security assessment should consider:
- Model providers
- Third-party AI APIs
- Open-source components
- Plugins and tools
- Training and retrieval data
- Model dependencies
- AI infrastructure
- Third-party integrations
- Data flows between providers
NIST’s AI RMF emphasizes that AI risk extends beyond an individual model or application into the broader ecosystem.
For enterprises using multiple AI vendors, third-party AI security validation should be part of the assessment strategy, not an afterthought.
9. Can you detect and respond to an AI attack?
Finding a vulnerability is only part of the problem.
Can your security team detect when someone is exploiting it?
AI security testing should evaluate whether you can identify:
- Prompt injection attempts
- Suspicious model behavior
- Unauthorized tool calls
- Abnormal API activity
- Sensitive-data extraction
- Privilege escalation
- Agent behavior outside defined boundaries
- Repeated jailbreak attempts
- Suspicious data retrieval
This becomes increasingly important as AI systems move into production.
Google Cloud’s 2026 AI Risk and Resilience report notes that enterprise AI has shifted toward autonomous, agentic systems capable of executing API calls and complex workflows, while attack paths increasingly include indirect prompt injection and AI supply-chain compromise.
If your AI can act, your security team needs visibility into what it is doing.
10. Can you prove that the AI security controls actually work?
This is where AI security testing needs to connect with governance and compliance. Having an AI security policy does not demonstrate that the system is secure.
Having a prompt filter does not prove that prompt injection cannot bypass it. Having role-based access does not prove that an AI agent cannot misuse those permissions. Security assurance requires evidence. That means documenting:
- What was tested
- Which attack scenarios were used
- Which vulnerabilities were identified
- What could actually be exploited
- What data or systems were exposed
- What remediation was implemented
- Whether fixes were retested
- How ongoing monitoring will work
NIST’s AI RMF provides a structured approach for managing AI risks, while frameworks such as ISO/IEC 42001 provide an organization-wide management-system approach to AI governance.
The strongest AI security programs connect risk assessment, technical testing, remediation, governance, and continuous monitoring rather than treating them as separate activities.
What Should an AI Security Assessment Actually Deliver?
A meaningful assessment should leave your security team with more than a list of vulnerabilities. It should provide a clear view of:
- AI attack surface: What AI systems, models, applications, APIs, agents, tools, data stores, and integrations need protection?
- Attack paths: What can an attacker actually achieve by exploiting an AI weakness?
- Business impact: Could an issue lead to data exposure, unauthorized access, financial loss, regulatory exposure, or operational disruption?
- Control effectiveness: Are your existing safeguards actually preventing or detecting the attack?
- Remediation priorities: Which vulnerabilities need to be addressed first?
- Validation: Did remediation actually eliminate the vulnerability?
For organizations deploying AI at scale, the assessment may also need to connect with broader AI risk management, third-party AI validation, ISO/IEC 42001, NIST AI RMF, HITRUST AI security requirements, and other applicable compliance obligations.
Why Accorian for AI Security Assessment?
AI security cannot be evaluated effectively by looking at the model in isolation. Accorian combines AI security testing, penetration testing, AI risk, governance, and compliance expertise to assess the complete attack surface. Our AI security capabilities include:
- AI Security Assessments
- LLM Penetration Testing
- AI Chatbot Penetration Testing
- Prompt Injection Testing
- AI Threat Modeling
- AI Red Teaming
- RAG Security Assessment
- Agentic AI Security
- MCP Security Assessment
- Third-Party AI Security Validation
- AI Risk Assessment
- AI Security Governance
Accorian’s AI security methodology can evaluate the AI application, model behavior, data, RAG architecture, APIs, identities, agents, connected tools, and supporting infrastructure.
Accorian is also one of 10 accredited firms offering both audit and testing services on a unified platform, with 450+ clients, 200+ frameworks, 96% client retention, and 65% evidence reusability.
That combination matters because identifying a vulnerability is only the beginning.
You need to know what it means, how to fix it, and how to prove the fix works.
Is Your AI Actually Secure?
You should not have to assume your AI is secure because:
- The model performs well.
- Your vendor says guardrails are enabled.
- Your application passed a conventional penetration test.
- Your AI policy has been approved.
- Your users have authentication.
- Your model provider has security certifications.
You should know because you have tested the system against realistic attack scenarios.
Before an attacker discovers what your AI can access, manipulate, or execute, find out yourself. Accorian can assess your AI attack surface, validate exploitable weaknesses, and help your security team move from AI adoption to secure AI deployment.
Get Your AI Security Assessment Scope
If you’re deploying an LLM, AI chatbot, RAG application, copilot, or AI agent, talk to Accorian about the right assessment scope for your architecture.
Test the AI before an attacker does.
