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Industrial control systems security for 2026

Penetration Testing in the Age of AI and Large Language Models

Organizations are rapidly integrating artificial intelligence and large language models into core business operations. AI assistants now support customer service, automate analytics, assist software development, and power internal knowledge systems.

Adoption is happening at remarkable speed. Recent research shows that 71 percent of companies already use generative AI in at least one business function, and adoption continues to expand across marketing, engineering, and service operations.

At the same time, enterprises are embedding AI deeper into operational workflows. Analysts predict that 40 percent of enterprise applications will include AI agents by 2026, transforming assistants into systems capable of executing multi step business tasks.

This rapid expansion introduces a new security reality.

AI systems behave differently from traditional software. They interpret natural language inputs, retrieve data from multiple sources, and dynamically generate outputs. These capabilities introduce new attack paths that traditional penetration testing approaches were never designed to evaluate.

As organizations deploy AI into production environments, penetration testing programs must expand to include AI enabled workflows, models, and infrastructure.

Why Traditional Testing Alone Is No Longer Enough

Traditional penetration testing focuses on vulnerabilities in applications, networks, and infrastructure. These tests typically evaluate authentication controls, input validation, software vulnerabilities, and configuration weaknesses.

AI systems introduce additional layers of complexity.

Large language models process instructions in natural language rather than structured inputs. They may retrieve information from internal databases, external APIs, or enterprise knowledge systems. Many are also integrated into workflows that can trigger actions such as sending emails, retrieving files, executing scripts, or modifying records.

This means that the security of an AI system depends not only on the infrastructure hosting the model but also on how the model interprets instructions and interacts with connected systems.

A penetration test that excludes AI components may miss critical pathways attackers could exploit.

Key AI Risks That Penetration Testing Must Evaluate

Security teams are increasingly focused on several risk categories that are unique to AI-enabled systems.

Aerstone Teal Number 1

Prompt Manipulation

Prompt manipulation occurs when attackers craft inputs designed to override or bypass the system’s intended behavior.

Because large language models attempt to interpret intent, malicious prompts may attempt to instruct the model to ignore security rules, expose hidden instructions, or retrieve restricted information.

A penetration test should evaluate how effectively system prompts, guardrails, and application controls resist adversarial inputs.

Aerstone Teal 2

Unauthorized Data Exposure

AI assistants frequently access internal knowledge bases, documentation, ticketing systems, or operational data to provide answers.

If retrieval mechanisms are poorly designed, attackers may be able to extract information outside their authorized scope.

Penetration testing should assess whether the model can be manipulated into revealing sensitive data from connected systems or training sources.

Aerstone Teal 3

Tool and Workflow Abuse

Many AI deployments connect models to tools that perform real-world actions.

Examples include systems that can retrieve files, access databases, send emails, execute scripts, or interact with enterprise platforms.

If an attacker can manipulate prompts or model outputs, they may be able to trigger actions that bypass traditional authorization controls.

As AI agents become more common in enterprise workflows, these risks will grow significantly. Analysts estimate that 23 percent of organizations are already scaling AI agents across business functions, with many more experimenting with them.

Testing should evaluate how AI systems interact with downstream tools and whether safeguards prevent unauthorized operations.

Aerstone Teal 4

Model Misuse and Abuse

Public-facing AI services are particularly susceptible to misuse. Attackers may attempt to use the system to generate malicious content, conduct reconnaissance, or automate activities that violate acceptable use policies.

A penetration test should examine how effectively monitoring, rate limiting, and usage controls prevent abuse of AI capabilities.

Expanding the Scope of Penetration Testing

Organizations deploying AI should begin incorporating AI-specific testing into their existing penetration testing programs.

This does not replace traditional security testing. Instead, it expands the scope to include AI-driven workflows and the infrastructure supporting them.

An effective AI penetration test may include:

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Evaluation of prompt handling and adversarial inputs

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Testing retrieval systems connected to internal data sources

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Assessing model guardrails and safety controls

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Examining integrations between AI systems and enterprise tools

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Reviewing access controls governing AI-generated actions

These tests help determine whether an attacker could manipulate AI behavior in ways that bypass standard security defenses.

The Importance of Testing AI Early

AI deployments often begin as productivity experiments inside departments. Over time, these tools evolve into operational systems that influence customer interactions, business decisions, and internal processes.

Security reviews frequently lag behind this rapid adoption. Organizations that assess AI systems early can identify weaknesses before those systems become deeply embedded in business workflows. Testing during deployment also allows teams to design safer architectures, limit access to sensitive data, and implement monitoring controls that detect abuse.

Waiting until AI systems are widely deployed makes remediation significantly more difficult.

The Future of Penetration Testing

The rapid adoption of AI is reshaping how security professionals think about testing.

Penetration testing is evolving from purely infrastructure and application-focused assessments to evaluations of intelligent systems that interpret language, access data, and automate actions.

As organizations continue integrating large language models into everyday operations, testing programs must adapt to ensure these systems remain secure, reliable, and resistant to manipulation.

The organizations that treat AI security as part of their penetration testing strategy today will be far better positioned to manage the risks of tomorrow’s AI-driven environments.

    If your AI can access it, attackers can too

    Find out how far that access really goes with AI-focused penetration testing. We simulate real-world attack paths across prompts, integrations, and data flows to show exactly what your AI can reach, expose, and execute.

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