July 31, 2026
ICS & OT Penetration Testing: Securing Industrial Systems
ICS & OT Penetration Testing: Securing Industrial Systems
Fiza Nadeem
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AI systems are becoming part of customer service, fraud detection, healthcare, and other business-critical workflows. But securing these systems requires more than checking code and infrastructure for conventional vulnerabilities.
This has increased the need for both AI red teaming and AI penetration testing. While the two approaches share several techniques, they are not identical. AI penetration testing generally focuses on discovering and validating exploitable vulnerabilities within a defined technical scope.
AI red teaming takes a broader adversarial approach, testing how an AI system behaves when an attacker actively tries to manipulate, misuse, or compromise it.
Understanding the difference helps organizations choose the right type of security testing. It can also show when both approaches should be combined as part of a broader AI security assessment.
No. AI red teaming and AI penetration testing are related but serve different purposes. AI penetration testing focuses on finding and validating security vulnerabilities in an AI application, model environment, APIs, infrastructure, and related components.
AI red teaming focuses more heavily on adversarial behavior, realistic attack objectives, model misuse, and the potential impact of successful attack paths.
Both are authorized offensive security activities. Both can assess LLM applications, AI APIs, RAG systems, AI agents, and supporting infrastructure. They may also use overlapping techniques, such as prompt injection, access control testing, API exploitation, and data exposure attempts.
The main difference is the testing objective and perspective.
A penetration test typically asks:
What vulnerabilities exist, and can they be exploited?
An AI red team exercise asks:
What can an attacker accomplish by deliberately manipulating the AI system and combining weaknesses across its environment?
NIST describes AI red teaming as a structured effort to find flaws, vulnerabilities, and undesirable system behavior through controlled testing and adversarial methods. Its AI security work also distinguishes broader red-team activities from testing focused on a specific application or system.
In practice, the two approaches can complement each other. Penetration testing can establish the technical weaknesses of an AI environment, while red teaming can show how those weaknesses could be used to achieve a meaningful attacker objective.
AI red teaming is an adversarial security testing approach used to evaluate how an AI system behaves under intentional attack, misuse, and manipulation.
It does not stop at identifying a technical weakness. Testers attempt to understand how the AI system responds to hostile inputs and how those responses could affect users, data, business processes, or connected systems.
An AI red team may examine the model itself, the application around it, the data it consumes, the tools it can access, and the downstream systems affected by its decisions or outputs.
This broader approach is important because AI systems often have dynamic attack surfaces. A model can behave differently depending on the prompt, conversation history, retrieved context, user permissions, external data, or tools available to it.
The goal of AI red teaming is not simply to create a list of vulnerabilities. It is to understand the resilience of the complete AI system under adversarial pressure. A red team may ask:
This objective-driven approach can reveal weaknesses that may not be obvious when each component is tested separately. NIST research on AI agents, for example, has shown how indirect prompt injection can cause agents to perform unintended actions when they process malicious instructions embedded in external data.
ioSENTRIX similarly describes AI red teaming as testing model behavior, decision integrity, data dependencies, APIs, integrations, and AI-driven workflows rather than focusing only on static technical vulnerabilities.
The exact scope depends on the AI architecture and business use case, but an AI red team may evaluate:
These risks are not limited to generative AI. NIST's adversarial machine learning taxonomy covers attacks such as evasion, poisoning, privacy, and misuse across different AI and ML systems.
AI penetration testing is a structured security assessment designed to discover and validate exploitable vulnerabilities across an AI application's technical attack surface.
It combines traditional penetration testing techniques with AI-specific security testing. Depending on the scope, this can include the AI application, model endpoints, APIs, authentication, authorization, cloud infrastructure, data stores, RAG components, and connected services.
The objective is to identify weaknesses that an attacker could exploit and provide evidence of their technical and business impact. AI penetration testing is therefore not limited to testing whether a model can be tricked. It also examines the security controls surrounding the model.
For example, an AI application may have strong model guardrails but expose an API with weak authorization. An attacker might not need to bypass the model at all if they can directly access sensitive resources through the application's API.
Depending on the environment, an AI penetration test can cover:
This broader technical coverage matters because an AI application is still an application. AI-specific controls do not remove the need for secure authentication, authorization, API design, infrastructure configuration, and data protection.
The difference becomes clearer when the two approaches are compared as testing processes rather than simple definitions.
AI red teaming starts with an attacker and an objective. The team considers realistic adversaries, valuable assets, likely attack paths, and the actions an attacker may want to achieve. The assessment can span the model, users, data, applications, tools, APIs, and downstream systems.
For example, the objective might be to determine whether an attacker can make an AI customer-support agent expose another customer's information or perform an unauthorized account action.
AI penetration testing usually starts with a defined technical scope and testing objectives. The team maps the agreed components, identifies vulnerabilities, tests those weaknesses, and validates their impact. The scope may include an AI application, API, cloud environment, model endpoint, or supporting infrastructure.
The approaches can overlap, but the starting question is different:
Red teaming: What can an attacker achieve?
Penetration testing: What vulnerabilities can be exploited?
Adversarial testing is central to AI red teaming. Testers may deliberately adapt their techniques based on how the AI responds. Depending on the system, testing can include:
This is especially important for AI agents. An agent may receive information from emails, websites, documents, or other external sources. If malicious instructions are embedded in that content, the agent may be manipulated into performing unintended actions.
NIST identifies this type of agent hijacking as a significant security evaluation challenge and emphasizes the need for adaptive testing as attack techniques evolve.
AI red teaming therefore looks beyond a single prompt or isolated response. It can examine how the entire AI workflow behaves across multiple interactions and attack stages.
AI penetration testing places strong emphasis on proving that a discovered weakness can actually be exploited.
For example, if a tester identifies a possible authorization flaw, they may attempt to access a resource belonging to another user and document the result. This helps distinguish a theoretical weakness from a practical security issue.
AI red teaming can take the same exploitation process further. Instead of stopping after proving one vulnerability, the red team may combine multiple weaknesses to determine what an attacker can ultimately accomplish.
For example:
Prompt injection → agent manipulation → excessive permissions → unauthorized tool action
Each weakness may have limited impact on its own. Together, they could create a serious business risk. That difference in perspective is one of the most important distinctions between AI red teaming and AI penetration testing.
AI red teaming can uncover behavioral and system-level risks that may receive less attention during a conventional vulnerability-focused assessment. This does not mean AI penetration testing cannot identify these issues. The difference is that red teaming is specifically designed to explore adversarial outcomes and attack paths.
Prompt injection occurs when an attacker manipulates an AI system through crafted instructions or untrusted content. The goal may be to override intended behavior, expose information, bypass safeguards, or influence downstream actions.
Jailbreaks are another form of adversarial testing in which attackers attempt to make a model bypass restrictions that normally limit certain outputs or actions.
AI red teaming can test these weaknesses through repeated and adaptive attempts rather than relying on a small set of predefined prompts. This matters because AI defenses can behave differently when attackers modify their approach.
NIST research has highlighted the importance of adaptive red teaming because models can become more resistant to known attacks while remaining vulnerable to new attacks designed specifically for them.
Retrieval-Augmented Generation, or RAG, allows AI applications to retrieve information from external knowledge sources before generating a response. This improves usefulness but also introduces another attack surface.
A red team can examine whether malicious documents, manipulated content, or weak retrieval controls can influence the model. Testing may determine whether an attacker can:
The risk becomes greater when the RAG system handles confidential enterprise data or connects to business-critical workflows.
AI agents can do more than generate text. They may call APIs, send emails, access databases, or perform other actions. This creates a major difference between a passive chatbot and an agent that can act on behalf of a user.
AI red teaming can test whether an attacker can manipulate an agent into using a legitimate tool for an illegitimate purpose. It can also assess whether the agent has unnecessary permissions.
For example, an agent that only needs to read customer records should not automatically have permission to modify them. Excessive permissions can turn a successful prompt injection into a real-world security incident.
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AI systems may have access to customer information, internal documents, source code, business data, or other sensitive material. Red teamers can attempt to determine whether malicious prompts, conversation manipulation, retrieval abuse, or application flaws can cause this information to leak.
Testing may include attempts to:
The goal is to understand not only whether data can leak, but also what conditions allow the leakage and what an attacker could gain from it.
AI systems are increasingly used to support decisions such as fraud detection, risk scoring, recommendations, identity verification, and content classification. An attacker may attempt to manipulate inputs so the system produces a favorable result.
AI red teaming can examine whether adversarial inputs can influence model decisions and whether those decisions trigger meaningful business consequences.
For high-impact AI systems, this type of testing is particularly important because a technically small model weakness can create a significant operational or financial impact.
Some of the most important AI security weaknesses only become visible when several vulnerabilities are combined. Consider an AI agent that has:
A traditional assessment might report each issue separately. A red team may combine them to determine whether the complete chain can result in unauthorized data access or an external action.
This is why AI red teaming can provide valuable insight into attack paths, rather than only individual vulnerabilities.
The comparison should not be one-sided. AI red teaming does not replace traditional application, API, cloud, or infrastructure security testing. An AI system can have strong model safeguards and still contain serious technical vulnerabilities.
AI penetration testing may uncover:
For example, an LLM may correctly refuse a malicious prompt, but the application hosting it could still expose an administrative API to unauthorized users.
Likewise, a RAG system may have good prompt-injection defenses but still allow a user to retrieve documents they should not be able to access because of an authorization flaw.
This leads to an important principle:
Securing the AI model does not automatically secure the application around it.
The right choice depends on what you want to learn from the assessment.
AI penetration testing is a strong fit when you:
It is particularly useful when the primary question is whether your AI environment contains exploitable technical weaknesses.
AI red teaming is more appropriate when you:
This approach is especially valuable for AI systems where the consequences of manipulation extend beyond the model itself.
For business-critical AI systems, combining both approaches often provides stronger coverage. Consider both when:
In these environments, penetration testing can identify exploitable weaknesses while red teaming can determine how those weaknesses might be combined to achieve a realistic attacker objective.
Yes. In many environments, combining them creates a more complete AI security testing strategy. Rather than treating AI red teaming and penetration testing as competing services, organizations can use them at different stages of the security lifecycle.
A practical approach can look like:
AI Security Assessment → AI Penetration Testing → AI Red Teaming → Remediation → Retesting
An initial AI security assessment can establish the architecture, attack surface, data flows, security controls, and key risks. AI penetration testing can then identify and validate technical vulnerabilities across the defined environment.
AI red teaming can build on those findings by simulating realistic attack scenarios and testing whether an attacker can combine weaknesses to reach important objectives. After remediation, retesting confirms whether identified issues have been addressed.
This layered approach can incorporate:
NIST's AI evaluation work similarly treats red teaming as one level within a broader evaluation process rather than as the only form of AI testing. The important point is that each activity answers a different security question:
Red teaming: What can an attacker achieve?
Penetration testing: What vulnerabilities can be exploited?
AI security assessment: What risks exist across the AI system and its environment?
An AI security assessment is broader than a single penetration test or red team exercise. It evaluates the security posture of an AI system across its architecture, data, models, applications, users, integrations, and operational environment.
Depending on the system, an assessment may cover:
AI red teaming can then serve as one important component of this larger assessment. For example, an organization may first map its AI architecture and identify key risks. The security team can then use penetration testing to validate technical weaknesses and red teaming to test whether those weaknesses can be turned into realistic attack paths.
This is consistent with the broader direction of AI security guidance. NIST recommends using red-team exercises to test AI systems under adversarial or stressful conditions, measure failure modes, and support continuous improvement.
The result is a more complete view of security than relying on any single testing method.
AI security requires more than applying conventional penetration testing tools to an AI application. The testing team needs to understand how models behave, how data flows through the system, how AI connects to applications and APIs, and how attackers could manipulate those relationships.
ioSENTRIX approaches AI security from an offensive security perspective, with AI red teaming and related testing focused on the risks introduced by models, data, applications, integrations, and AI-driven workflows.
Its AI security content and testing approach cover areas such as:
ioSENTRIX's AI red teaming approach specifically addresses model behavior, prompt injection, data poisoning, AI-driven automation, model hosting, APIs, integrations, and decision-related risks. Its LLM security guidance also highlights prompt injection testing, RAG access controls, tool-call authorization, data extraction attempts, and guardrail bypass testing.
The objective is not simply to generate a long list of findings. Effective AI security testing should help organizations understand which weaknesses matter, how they could be exploited, and what should be done next.
If you are unsure whether your environment requires AI penetration testing, AI red teaming, or a broader AI security assessment, ioSENTRIX can help determine the appropriate testing approach based on your AI architecture, use case, integrations, and risk profile.
Talk to our AI security experts to assess your environment and detect attack paths that matter most.
No. They overlap, but their objectives are different. AI penetration testing focuses on discovering and validating exploitable technical vulnerabilities. AI red teaming uses adversarial scenarios to evaluate how an attacker could manipulate the AI system, combine weaknesses, and achieve a realistic objective.
LLM red teaming focuses specifically on large language model applications and their associated risks. AI red teaming can have a broader scope that includes LLMs as well as other AI and ML systems, their data pipelines, applications, integrations, and decision workflows.
AI penetration testing is an important part of LLM security, but it may not cover every behavioral or adversarial risk. LLM applications can be affected by prompt injection, jailbreaks, RAG manipulation, data leakage, and agent or tool abuse. Combining penetration testing with adversarial testing can provide broader coverage.
AI red teaming should be considered before production deployment and whenever significant changes affect the AI system. Important triggers include new AI features, model changes, fine-tuning or training-data changes, new third-party models, major integrations, and significant changes to AI-driven workflows.
No. AI red teaming complements penetration testing. Red teaming focuses strongly on adversarial behavior and realistic attack objectives, while penetration testing provides structured identification and validation of technical vulnerabilities. Using both can provide a more complete view of AI security.
