July 12, 2026
Continuous Penetration Testing: Why Annual Pentests Aren’t Enough
Continuous Penetration Testing: Why Annual Pentests Aren’t Enough
Omair
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Omair Manzoor is the Founder, CEO, and Chief Hacker of ioSENTRIX, a CREST-accredited cybersecurity firm. With 14+ years in offensive security, he has led penetration tests for Fortune 500 companies including engagements for Amazon, Bank of America, VISA, and the London Underground. His exploits are part of the Metasploit Framework and Immunity Canvas. Omair is a published researcher, conference keynote speaker (DOD, CISA, BSides), and technical reviewer for academic security textbooks.
AI is becoming part of customer applications, internal workflows, and business automation. Large language models (LLMs), retrieval-augmented generation (RAG), APIs, and model infrastructure are now connected to data and systems that attackers may want to access or manipulate.
This creates security risks that traditional penetration testing may not fully address. An application can have secure authentication and APIs but still expose sensitive information through prompt injection.
An AI agent may also perform an unauthorized action because its tools or permissions are not properly restricted. Other risks include jailbreaks, model manipulation, model extraction, and insecure handling of AI-generated output.
That is why organizations evaluating AI penetration testing services need to look beyond a vendor's price, certifications, or automated scanning capabilities. The right provider should understand how AI systems behave, how they interact with applications and data, and how attackers can chain weaknesses across the AI stack.
So, how do I choose an AI penetration testing vendor?
The answer depends on several factors. You need to evaluate the provider's AI and LLM security expertise, testing methodology, attack surface coverage, use of automation, human validation, security frameworks, reporting quality, and retesting process.
AI penetration testing services are security assessments designed to identify and validate vulnerabilities in artificial intelligence and machine learning systems.
AI security testing also examines model behavior, AI-specific attack paths, data flows, prompts, integrations, and AI-driven decisions.
An AI penetration test can cover the complete technology stack surrounding an AI system. Depending on the architecture, this may include:
For example, a RAG application may connect an LLM to internal documents and a vector database. Testing only the chatbot interface would not provide a complete picture of its security.
A broader assessment should also examine whether an attacker can manipulate retrieved content, access restricted documents, or use prompt injection to influence the model.
ioSENTRIX's AI/ML and LLM penetration testing approach similarly covers areas such as data pipelines, model training, deployment environments, and API integrations rather than treating the model as an isolated component.
AI penetration testing does not replace conventional penetration testing. Instead, it extends security testing to address risks introduced by AI systems.
A traditional web application test may identify an access control flaw in an API. An AI security assessment can go further and determine whether the same API can be abused through an AI agent, whether the model can be manipulated into calling it, or whether the agent has more privileges than it needs.
The two forms of testing therefore work best together. Conventional penetration testing protects the underlying application and infrastructure, while specialized AI security testing evaluates the unique risks introduced by models and AI-driven functionality.
Choosing an AI penetration testing vendor should be treated as a security decision, not simply a procurement exercise.
A provider may advertise AI security testing without having the technical depth required to assess modern LLM, RAG, or agentic systems. A practical AI security vendor selection process should evaluate the following areas.
Do not assume that a vendor experienced in traditional penetration testing automatically has deep AI security expertise.
AI systems introduce attack paths that require knowledge of both offensive security and AI technologies. A capable provider should understand LLM behavior, machine learning security, AI application architecture, RAG systems, and AI agents.
Look for demonstrated experience with:
Ask the vendor to explain how it approaches a real AI environment rather than simply asking whether it "offers AI pentesting."
A useful buyer question is:
Can the vendor demonstrate previous AI security engagements, technical research, or a detailed methodology for testing systems similar to ours?
The provider should be able to explain what it tests, how it validates vulnerabilities, and how it determines the business impact of an AI-specific finding.
ioSENTRIX, for example, publishes research and testing guidance covering LLMs, AI/ML systems, AI red teaming, prompt injection, data poisoning, RAG security, and AI-driven penetration testing.
A strong LLM pentest should involve more than sending a few malicious prompts to a chatbot. The vendor should define a testing scope based on the way your AI system is built and used.
Depending on the environment, this may include:
The OWASP Top 10 for LLM Applications is a useful reference when evaluating the scope of an LLM security assessment. Its 2025 guidance provides a structured view of major risks affecting LLM applications.
However, a vendor should not simply claim that its assessment is "OWASP compliant." Ask how the framework influences the actual testing process. For example, if the vendor identifies excessive agency as a risk:
Those details separate a meaningful AI security assessment from a basic checklist.
One of the most important vendor-selection criteria is attack surface coverage. Testing only the LLM interface can leave important attack paths untouched.
Modern AI applications are usually made up of several interconnected components:
User → Application → LLM → RAG/Data → Tools/APIs → Enterprise Systems
Each connection can introduce security risks. A comprehensive AI security assessment may therefore need to examine:
RAG is a good example of why this matters. A RAG application retrieves information from external sources before passing relevant context to the LLM.
If those sources contain malicious documents, poisoned content, weak access controls, or sensitive information, the AI application may expose or misuse that information.
When comparing providers, ask:
Does the vendor test the AI model alone, or does it assess the complete AI application and its connected systems?
The second approach usually provides a much more realistic view of risk.
Another important factor is how the provider performs its testing. There are three common approaches:
For most organizations, a hybrid model provides the strongest balance.
Automation can help with:
Human testers are still important for:
This distinction matters because not every unusual model response represents a security vulnerability. A skilled tester needs to determine whether the behavior can actually be exploited and what an attacker could accomplish.
A credible AI security vendor should be able to map its methodology to recognized security frameworks and standards. This gives buyers a clearer way to understand what is being tested and how risks are categorized.
However, framework names alone should not determine your decision. The important question is how the provider uses them during the engagement.
The OWASP Top 10 for LLM Applications is one of the most useful references for organizations assessing LLM-based applications.
It addresses risks that can arise from the way LLMs process prompts, data, tools, and outputs. Depending on the application, relevant testing areas can include prompt injection, sensitive information disclosure, supply chain risks, excessive agency, and vector or embedding weaknesses.
MITRE ATLAS provides a knowledge base focused on adversarial tactics and techniques against AI-enabled systems. It is modeled after MITRE ATT&CK and is based on real-world attack observations and demonstrations from AI red teams and security groups.
For buyers, ATLAS can be particularly useful when evaluating an AI red team provider. It provides a structured way to think about adversary behavior and realistic attack scenarios.
The NIST AI Risk Management Framework (AI RMF) provides organizations with a structured approach to managing AI risks throughout the AI lifecycle.
Its core functions are Govern, Map, Measure, and Manage. NIST also provides a Generative AI Profile that addresses risks and considerations specific to generative AI systems.
AI penetration testing can contribute to this broader risk-management process by providing evidence about technical vulnerabilities and system behavior.
ISO/IEC 42001 is an international standard for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System. It provides a broader management and governance framework for organizations that develop, provide, or use AI systems.
It is important to understand that ISO/IEC 42001 is not a penetration testing methodology. Instead, it addresses AI management and governance. A security assessment can support an organization's wider AI risk-management and governance activities.
What Should Buyers Look For?
Do not ask only:
"Do you use OWASP, MITRE, NIST, or ISO?"
Ask:
Before choosing an AI penetration testing provider, ask questions that reveal its technical depth, methodology, and ability to work with your environment.
Ask:
The goal is to determine whether the provider has actual experience with systems similar to yours.
Ask:
Ask:
The answer should reflect your actual AI environment rather than a fixed package.
Ask:
A good report should help your team fix problems, not simply provide a list of vulnerabilities.
When evaluating an AI penetration testing provider, the same criteria used to compare vendors should also be applied to ioSENTRIX.
The company's current AI security and penetration testing services cover AI, ML, and LLM environments and emphasize expert-driven testing across data pipelines, model training, deployment environments, and API integrations.
AI security requires more than automated vulnerability discovery. Experienced testers need to understand the application's purpose, identify realistic abuse cases, and validate whether model behavior can lead to a meaningful security impact.
ioSENTRIX combines AI security knowledge with offensive security expertise to assess AI and LLM systems for risks such as prompt injection, data leakage, adversarial attacks, data poisoning, model abuse, and API exploitation.
A strong AI security assessment should examine the components around the model, not just the model itself. ioSENTRIX's AI/ML and LLM penetration testing coverage includes areas such as:
This broader approach helps identify vulnerabilities that may emerge between AI components rather than from the model alone.
Some organizations need more than a structured vulnerability assessment. They need to understand how an attacker could manipulate an AI system under realistic conditions.
ioSENTRIX's AI red teaming guidance focuses on threats such as prompt injection, data poisoning, model inference and extraction, and other adversarial techniques.
Our approach treats AI red teaming as a way to simulate realistic attacks against AI systems rather than simply running a vulnerability scan.
A penetration test is valuable only when the findings can be understood and addressed. ioSENTRIX states that its penetration testing engagements include detailed findings, proof-of-concept evidence, remediation strategies, and retesting options.
Our broader penetration testing service also emphasizes connecting technical vulnerabilities with business risks. For an AI security assessment, useful reporting should explain:
AI systems change quickly. Organizations may update models, modify prompts, add data sources, introduce new tools, change APIs, or connect agents to additional business systems.
That means an AI security assessment should not always be viewed as a one-time exercise. For example, adding a new tool to an AI agent can create a new attack path.
Updating a RAG knowledge base can introduce malicious or sensitive content. Changing a model can also affect how existing security controls behave. Continuous or recurring AI security testing can help organizations identify security gaps introduced by these changes.
ioSENTRIX's current AI security content also emphasizes ongoing testing and monitoring as AI environments evolve, particularly for RAG and other systems that continuously interact with new data and workflows.
Start by evaluating the provider's AI and LLM security experience, testing methodology, attack surface coverage, human expertise, framework alignment, reporting quality, and retesting process. Confirm that the vendor can test your specific architecture, including RAG systems, AI agents, and connected applications where applicable.
Look for comprehensive AI security testing rather than prompt-only testing. The provider should be able to assess AI models, applications, APIs, data sources, RAG pipelines, integrations, authentication, authorization, and AI-driven workflows.
LLM security testing focuses specifically on large language models and LLM-powered applications. AI penetration testing can have a broader scope that includes ML models, training pipelines, model hosting, AI applications, APIs, data pipelines, RAG systems, and other AI infrastructure.
Choose based on your objective. AI penetration testing is useful when you need to identify and validate exploitable vulnerabilities across an AI system. AI red teaming is more focused on adversarial simulation and testing how the system responds to realistic attack scenarios.
No. Automation can improve testing speed, scale, and coverage, but it may not understand application-specific business logic, complex attack chains, or the real-world impact of a model behavior. A hybrid approach that combines automation with experienced human validation can provide deeper security coverage.
