Enterprise AI is no longer limited to experiments and internal pilots. Organizations are deploying LLM applications, AI copilots, RAG systems, customer-facing chatbots, autonomous agents, and AI-enabled workflows that can access sensitive data and interact with business-critical systems.
That creates a security problem traditional application testing cannot always answer.
Can an attacker manipulate the AI into exposing sensitive data, bypassing authorization, abusing connected tools, or taking an action it was never intended to take?
An enterprise AI security assessment is designed to answer that question.
In 2026, AI security testing has also moved beyond basic jailbreak testing. Modern assessments may need to evaluate the AI application, model behavior, RAG pipeline, APIs, agents, tools, permissions, integrations, data, and supporting infrastructure. OWASP’s 2026 vendor evaluation criteria specifically distinguishes meaningful adversarial testing from superficial “jailbreak-only” approaches and includes modern architectures such as tool-calling agents, MCP, and multi-agent workflows.
If your organization is evaluating AI security assessment companies, AI penetration testing providers, or AI red teaming services, this guide explains what to look for and how leading providers differ.
What Is an AI Security Assessment for an Enterprise?
An AI security assessment is a technical evaluation of an AI system and its surrounding environment to determine whether attackers can exploit, manipulate, or misuse it. Depending on the architecture, testing can cover:
- LLM and model security
- Prompt injection and jailbreaks
- Sensitive information disclosure
- System prompt leakage
- RAG and vector database security
- Data and model poisoning
- AI agent security
- Excessive agency and permissions
- API and integration security
- Authentication and authorization
- Insecure output handling
- AI supply-chain risks
- Cloud and application security
- Connected tools and MCP
- Multi-agent workflows
- Logging, monitoring, and security controls
The important distinction is that an enterprise assessment should not stop at identifying a theoretical AI vulnerability.
It should determine what an attacker can actually achieve.
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
Accorian’s current AI security assessment guidance similarly emphasizes testing the complete AI attack surface, including prompts, models, data, APIs, RAG, tools, agents, integrations, and supporting infrastructure.
Why Is Enterprise AI Security Testing Different From Traditional Penetration Testing?
Traditional penetration testing remains essential because an AI application still contains APIs, authentication mechanisms, databases, cloud infrastructure, web interfaces, and integrations.
But AI introduces another layer of attack surface:
User → Prompt → Model → RAG → Data → API → Tool → Agent → Business System
An attacker may manipulate one layer to compromise another.
A conventional application penetration test may identify an insecure API. An AI security assessment can additionally determine whether an attacker can manipulate an AI agent into reaching that API or abusing permissions that were never intended to be exposed through natural-language interaction.
This is why AI security testing should complement rather than replace traditional application, API, cloud, and infrastructure security testing.
What Should Enterprises Look for in an AI Security Assessment Company?
Choosing an AI security testing provider should involve more than comparing the number of vulnerabilities a vendor says it can detect. A serious enterprise evaluation should consider at least seven areas.
- AI-specific offensive security expertise
The provider should demonstrate hands-on experience testing prompt injection, jailbreaks, sensitive information disclosure, system prompt leakage, RAG vulnerabilities, model manipulation, excessive agency, and AI-specific attack paths.
- Full-stack testing
The assessment should extend beyond the chatbot interface or model endpoint. The provider should be able to evaluate:
AI model + application + APIs + identity + data + RAG + agents + tools + infrastructure
- Agent and tool security
If your AI can search databases, call APIs, execute code, send messages, update records, or trigger workflows, the assessment should test whether those capabilities can be manipulated.
This is particularly important as enterprises move from generative AI toward agentic AI. Accorian’s research on AI agents highlights the security implications of agents that can call APIs, update databases, search knowledge bases, and perform actions across connected systems.
- Real attack-path validation
A provider should demonstrate whether individual weaknesses can be chained into meaningful compromise. A report saying “prompt injection detected” is less useful than demonstrating:
How the injection works → what control it bypasses → what data or system it reaches → what an attacker could ultimately achieve.
- Human-led testing
Automated AI security tools can provide useful scale, particularly for repetitive jailbreak and prompt testing. But enterprise environments often require human judgment to identify cross-layer attack paths and business impact.
Bishop Fox, for example, describes combining hands-on AI/LLM testing with broader application, API, cloud, and red-team testing rather than limiting the assessment to surface-level AI checks.
- Enterprise reporting and remediation
Look for reports that include:
- Evidence
- Reproduction steps
- Attack paths
- Business impact
- Severity
- Remediation recommendations
- Retesting
The objective is not simply to produce a vulnerability list. It is to help security and engineering teams reduce actual risk.
- Ability to support the broader AI security program
As AI becomes part of enterprise operations, technical testing may need to connect with:
- AI risk management
- AI governance
- Third-party AI risk
- ISO/IEC 42001
- NIST AI RMF
- Security policies
- Compliance requirements
- Continuous monitoring
This becomes particularly valuable for organizations that want one security partner across technical testing and AI governance.
Which Are the Best AI Security Assessment Companies for Enterprises in 2026?
There is no single AI security assessment provider that is objectively best for every enterprise. The right choice depends on your AI architecture, testing objectives, regulatory requirements, and whether you need technical testing alone or broader AI risk and governance support.
The following companies have publicly documented enterprise-relevant AI security capabilities and represent different approaches to the market.
1. Accorian
Best suited for: Enterprises looking to combine AI security testing with broader cybersecurity, AI risk, governance, and compliance capabilities.
Accorian takes an end-to-end approach to AI security, covering both AI-specific vulnerabilities and the traditional security environment surrounding enterprise AI. Accorian’s current 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
- Agentic AI Penetration Testing
- MCP Security Assessments
- Third-Party AI Security Validation
- AI Vendor Risk Assessment
- AI Governance
- NIST AI RMF Alignment
- ISO 42001 Advisory
- AI Security Controls and Policy Development
Accorian’s assessment approach covers the AI application, model, prompts, data, RAG architecture, APIs, agents, tools, integrations, and supporting infrastructure. The objective is to identify realistic attack paths rather than simply flagging isolated AI vulnerabilities.
Accorian also separates AI Security Assessments from AI Risk Assessments.
An AI Security Assessment asks:
Can this AI system be attacked, exploited, or manipulated?
An AI Risk Assessment asks:
What risks does this AI create for the organization, customers, data, operations, and compliance posture?
The two can work together as part of a broader AI security program.
Why enterprises may consider Accorian
Accorian’s differentiation is not limited to AI testing.
The company provides cybersecurity, penetration testing, compliance, risk assessment, third-party risk management, and vCISO services alongside its AI security practice. It also operates GORICO, its AI-enabled GRC platform, which currently supports 200+ frameworks and reports 65% evidence reusability.
That allows organizations to connect:
AI Security Testing → Risk → Remediation → Controls → Evidence → Governance
rather than managing each activity as a separate initiative.
Accorian also provides third-party AI security validation for organizations that rely on external AI providers. Its validation service evaluates technical controls, governance practices, and operational procedures across the AI ecosystem.
Consider Accorian if you need: AI security testing combined with enterprise cybersecurity, AI risk, governance, compliance, and ongoing program support.
2. IBM Consulting and Palo Alto Networks
IBM Consulting and Palo Alto Networks introduced a Rapid AI Security Assessment in 2026 designed to discover and assess AI infrastructure and prioritize risks across cloud environments. The assessment includes AI infrastructure discovery and inventory, shadow AI detection, security posture evaluation, and a prioritized roadmap aligned with frameworks such as NIST AI RMF, the EU AI Act, and ISO/IEC 42001. IBM has also introduced an enterprise cybersecurity assessment focused on readiness for threats enabled by frontier AI models, including AI-specific exposures and potential exploit paths.
3. Bishop Fox
Bishop Fox provides AI/LLM security assessments covering risks such as prompt injection, model extraction, data poisoning, resource exhaustion, supply-chain compromise, trust boundaries, isolation, and secrets management.
Its AI-focused red-team work can extend beyond the model into applications, cloud infrastructure, and multi-stage attack scenarios.
4. Praetorian
Praetorian publicly lists AI/ML penetration testing alongside application penetration testing, attack-path mapping, cloud penetration testing, purple teaming, and red teaming.
This broader offensive-security model can be relevant for enterprises that want AI testing incorporated into a wider attack-path and penetration-testing program.
5. Coalfire
Coalfire provides assessment services across major security and compliance frameworks and has expanded its AI-focused offerings. Its current services include AI framework assessment coverage for ISO/IEC 42001, NIST AI RMF, HITRUST, and other requirements.
Coalfire also provides ISO/IEC 42001 readiness and certification services, making it relevant for organizations combining AI governance and assurance requirements with their broader security program.
Which Type of AI Security Assessment Does Your Enterprise Need?
“AI security assessment” can refer to several different types of engagements. Choosing the wrong scope can leave important parts of your AI attack surface untested.
- AI Security Assessment: A broad technical evaluation of the AI system and surrounding environment.
Best for: Enterprises that want to understand their overall AI attack surface.
- LLM Penetration Testing: Focused offensive testing of an LLM-powered application.
Best for: Chatbots, copilots, RAG applications, and LLM-enabled SaaS products.
- AI Red Teaming: Adversarial testing designed around realistic attacker objectives and multi-step attack paths.
Best for: Mature AI deployments, high-value systems, and AI applications connected to sensitive enterprise infrastructure.
- AI Agent Security Assessment: Testing of AI agents, tools, permissions, APIs, memory, and autonomous actions.
Best for: Agentic AI systems capable of interacting with enterprise systems.
- AI Risk Assessment: A broader evaluation of privacy, regulatory, operational, ethical, business, and governance risks.
Best for: Organizations establishing an enterprise AI risk and governance program.
A mature AI security program may use several of these approaches across the AI lifecycle:
AI Risk Assessment → Threat Modeling → AI Security Testing → Red Teaming → Remediation → Retesting → Continuous Monitoring
What Should an Enterprise AI Security Assessment Test?
The scope should depend on the architecture, but most enterprise assessments should consider the following layers.
Model and LLM layer
Test for:
- Prompt injection
- Jailbreaks
- System prompt leakage
- Sensitive information disclosure
- Model manipulation
- Unsafe outputs
RAG and data layer
Test:
- Retrieval authorization
- Cross-tenant data access
- Sensitive document exposure
- Vector database security
- Indirect prompt injection
- Data poisoning
- Access-control failures
Application and API layer
Test:
- Authentication
- Authorization
- API security
- Session controls
- Input validation
- Insecure output handling
- Business logic
Agent and tool layer
Test:
- Excessive agency
- Tool misuse
- Privilege escalation
- Unauthorized API calls
- Excessive permissions
- Agent goal manipulation
- MCP security
- Cross-system attack paths
Infrastructure layer
Test:
- Cloud configuration
- Identity and access management
- Secrets
- Model infrastructure
- CI/CD
- AI supply chain
- Logging and monitoring
This full-stack approach matters because the most serious enterprise AI vulnerabilities often emerge from interactions between components, rather than from the model alone.
How Much Does an Enterprise AI Security Assessment Cost?
There is no universal price for an enterprise AI security assessment. Cost depends on:
- Number of AI applications
- Number of models and providers
- RAG architecture
- APIs and integrations
- AI agents
- Connected tools
- Data sensitivity
- User roles
- Application complexity
- Cloud and infrastructure scope
- Testing depth
- Red-team requirements
- Retesting
A public chatbot should not have the same scope as an AI agent that can access customer records and execute privileged API calls.
When comparing proposals, ask:
What exactly is being tested?
A questionnaire or automated scan is not equivalent to an expert-led assessment that attempts to exploit the system and validate real business impact.
How Long Does an Enterprise AI Security Assessment Take?
The timeline depends on the scope and architecture. A single chatbot with limited integrations can require significantly less testing than an enterprise AI ecosystem containing multiple applications, RAG pipelines, agents, APIs, sensitive data, and cloud infrastructure. The provider should define the timeline based on:
- Number of AI systems
- Testing objectives
- Source-code availability
- Agent capabilities
- RAG architecture
- Number of integrations
- Infrastructure scope
- Required retesting
Do not choose a provider simply because it promises the shortest assessment.
The more useful question is:
- Which attack surfaces will actually be validated during the engagement?
- When Should an Enterprise Perform an AI Security Assessment?
AI security testing should ideally happen before production deployment. It should also be repeated after material changes, including:
- Launching a new AI application
- Changing the underlying model
- Adding RAG
- Adding a new API or integration
- Introducing an AI agent
- Increasing agent permissions
- Connecting sensitive data
- Changing authentication or authorization
- Adding a third-party AI provider
- Making significant architecture changes
AI security is not a one-time checkbox. The attack surface changes as the AI system changes.
What Should an AI Security Assessment Report Include?
A strong enterprise assessment should give both security leadership and engineering teams actionable information.
Look for:
- Executive summary: What does the organization need to know?
- Technical findings: What vulnerability was identified?
- Proof of exploitation: Can the finding be reproduced?
- Attack path: How can an attacker move from the initial weakness to meaningful impact?
- Business impact: What data, systems, customers, or operations are exposed?
- Prioritization: Which findings need immediate remediation?
- Remediation: What should the engineering and security teams change?
- Retesting: Has the fix actually worked?
The report should make it possible to move from:
Finding → Risk → Remediation → Validation
rather than simply producing a list of AI vulnerabilities.
What Has Accorian Found in Real-World AI Security Testing?
Accorian has tested more than 100 real-world AI chatbots and identified several recurring security weaknesses within its assessed sample:
- 82% showed prompt injection exposure
- 61% showed internal instruction exposure
- 49% showed jailbreak bypass
- 35% showed PII exposure
These figures represent Accorian’s assessed sample and should not be interpreted as universal prevalence across all AI applications. The significance is not that every enterprise AI system will contain these vulnerabilities. It is that AI security controls should be tested rather than assumed to work.
Why Choose Accorian for Enterprise AI Security Assessment?
Enterprise AI security requires more than checking whether a chatbot can be jailbroken. Accorian evaluates the broader AI attack surface, including:
Models → Applications → Data → RAG → APIs → Agents → Tools → Integrations → Infrastructure
Its AI security practice combines technical assessment with broader cybersecurity, risk, governance, and compliance capabilities. For enterprises, this means an engagement can extend from:
AI Threat Modeling
to
AI Security Assessment
to
AI Red Teaming
to
Remediation and Retesting
and, where required:
AI Risk Management + ISO 42001 + NIST AI RMF + AI Governance
Accorian also provides third-party AI security validation for organizations that rely on external AI vendors, evaluating technical controls, governance practices, and operational procedures across the AI ecosystem. For organizations that need to operationalize risk and compliance after the assessment, GORICO, Accorian’s AI-enabled GRC platform, supports 200+ frameworks and reports 65% evidence reusability.
This creates a broader security lifecycle:
Discover → Assess → Exploit → Remediate → Validate → Govern
rather than treating AI security testing as a standalone report.
Is Your Enterprise AI Ready for an Attack?
If your organization is deploying any of the following, an AI security assessment should be part of the security lifecycle:
- Customer-facing AI chatbot
- Enterprise copilot
- RAG application
- AI-powered SaaS product
- AI agent
- AI-enabled workflow
- LLM application
- AI system connected to APIs
- AI system accessing confidential or regulated data
- Third-party AI platform used in critical operations
The critical question is not:
“Does our AI have guardrails?”
It is:
“Can an attacker manipulate our AI into crossing a security boundary?”
That could mean accessing another customer’s data, retrieving an internal document, bypassing authorization, extracting sensitive information, abusing an API, or triggering an action the AI was never supposed to perform. A properly scoped enterprise AI security assessment is designed to find out before an attacker does.
Get an Enterprise AI Security Assessment
Accorian helps enterprises assess and test AI applications, LLMs, RAG architectures, AI agents, APIs, integrations, and AI-specific attack paths.
Whether you need AI penetration testing, AI red teaming, prompt injection testing, agentic AI security assessment, MCP security testing, AI risk assessment, or enterprise AI governance, the engagement can be scoped around your actual architecture and business risk.
Ready to test your AI before attackers do?
