Penetration test reports are often extensive, spanning hundreds of pages detailing scenarios, vulnerabilities, and response strategies. Unsurprisingly, security teams usually struggle to interpret them efficiently. Key questions arise: Is the report good or bad? What are the critical takeaways? How should the evaluation process begin? How are these findings related to my business context?
As of 2023, 70% of companies that perform penetration tests do so to support vulnerability management programs, 69% to assess security posture, and 67% to achieve compliance. Yet, as software and infrastructure grow more complex, leaders must decide where to focus limited resources. While existing tools help, they remain constrained by scope, budget, and operational limitations.
Recent developments in LLMs and AI agents offer a solution. It processes alerts, synthesizes data, and automates context-aware testing tailored to business risks. A well-deployed Agnetic AI-driven pen-testing tool could cover numerous tests. But can it truly think outside the box?
AI agents are any AI-powered algorithm or model autonomous enough to make judgments and take actions. They can usually sense other AI agents and the environment in which they operate, learn, and consider human input.
Agenic pen testing utilizes these AI agents to conduct continuous offensive security testing. They can emulate human processes, from alert interpretation to decision-making. As new information is continually added, the agent’s capabilities are updated as fast as the threat databases.
While automated penetration testing tools are continuous and scalable, they often lack integrated business logic, application context, and real-time adaptability. This lack of context and adaptability results in too many false positives and may only scratch the surface of deeper vulnerabilities.
On the other hand, agentic AI blends the adaptive reasoning of humans (which remains crucial) with the strictness of code. Some ways in which AI agents can be more efficient than traditional penetration testing include:
Evaluating any penetration testing depends on many factors, including the scope, time, and desired results. With that in mind, here are a few areas where AI agents are far more efficient than traditional human pen testers:
Currently, there are about 200,000 vulnerabilities cataloged in the CVE database, about 60,000 of which have a CVSS Score higher than 8. Since your codebase likely spans hundreds of thousands of lines, manually identifying each potential vulnerability is overwhelming. When a human tester completes a review, the code will have changed, requiring another analysis round. This is an exhausting and inefficient process.
An agent can review your internal and open-source code for vulnerabilities and check if any needed patches have been applied. Importantly, it does this faster and more efficiently without accidentally missing an off-branched code branch.
It can also check each software supply chain link in your open-source code snippets. As your app code is constantly updated, this type of task should be continuously performed. Maintaining an updated SBOM for each live version would make this process faster and more transparent.
Developers and QA testers may be able to stop some threats. However, spotting the ones no one has thought of is much more challenging. It’s up to the security team, who is knowledgeable in security practices and threats, to develop tests that look for hidden weaknesses.
AI models can enhance the human element by learning from their experience and the aggregated experience of existing threat databases. If a new threat is identified online, AI agents can quickly integrate it into their testing strategy. They stay updated through retraining, fine-tuning, and real-time threat feeds from sources like MITRE ATT&CK. This continuous learning component makes your security more adaptable and capable of mitigating new threats.
You can customize agents to meet any need, whether that’s regulatory compliance, defending against data exfiltration, or identifying misconfigurations. For example, Terra’s AI agentic pen testing tool offers context-aware findings and responses, allowing you to define how each finding should be handled based on business priorities.
By embedding business logic into security testing, you can reduce noise and let security teams act on the most pressing risks first. Plus, built-in customization lets you quickly adjust responses as priorities shift. This adaptability is essential for defending against dynamic threats like API security vulnerabilities in modern web applications.
The scope of traditional pen tests is significant. As attack vectors become more widespread, getting a human team to pen test every single app component is unfeasible and practically impossible. Even if you choose to use automated tools, they are limited in scope and usually superficial, generating too many false positives.
An AI agentic tool can place agents in any workflow and environment, allowing for far greater coverage of your systems. The only limit is your imagination.
The bigger the company, the more complex its system is and the more threats it must defend against. Hackers may consider it a ‘badge of honor’ to successfully compromise giants, but no company operates in isolation.
With the advent of software supply chain vulnerabilities, one company’s compromise can lead to thousands of other companies being penetrated (as attacks like the SolarWinds tell us).
This makes it even more critical to follow NIST’s 800-171 Incident Response Plan & Reporting Requirements, which require that incidents be reported within 72 hours. It is also crucial to test web applications to uncover vulnerabilities that, if exploited, could provide access to the organization’s broader network and potentially affect partners.
The more the cybersecurity community shares, the better we can prepare for similar attacks. Threat intelligence flows through forums, news sites, and knowledge bases, revealing key insights like active threat groups or emerging attack plans from dark web activity. All of this threat information could be incorporated into your agent’s defense goals in real-time, making your defenses as accurate and relevant to your business sector and potential threats as possible.
AI agents are still AI. As such, most of us tend to mistrust them, especially when we don’t know how they ‘think.’ That mistrust, along with AI’s lack of imagination and ‘out-of-the-box thinking,’ are some of the main reasons human testers still conduct most penetration tests today.
A human should always be in the loop, reviewing the reports and approving the actions and tests AI agents will perform. However, delegating the tedious part of the work to autonomous agents is a logical next step, considering the scope and complexity of the systems involved. As a bonus, agents are far easier to work with and can change their behavior quickly without any ego or additional meetings required to achieve the new goal.
Defining the scope of work is crucial in any penetration testing. Testers need to know precisely which system they're trying to breach, the goal if successful, and the allocated time frame (more time often means higher costs).
AI agents, however, offer a more efficient solution. They can be deployed across any system you choose, and you can instruct them on what to test at any time, making the testing process far more flexible. Adding a system to a human-run test is often complex and time-consuming, whereas integrating agents into another pipeline is almost frictionless.
Given their capabilities, broad coverage, and adaptability, AI agents clearly outperform human testers, especially when dealing with larger, more complex systems that are difficult for manual testing to fully cover.
A penetration test is very different than a security assessment. While assessments scan for vulnerabilities, pen tests actively exploit them to demonstrate potential data exfiltration, sabotage, or system hijacking. It’s not a simple pass-or-fail test but a tool to refine security based on risk, priorities, and resources. A flood of unprioritized vulnerabilities only leads to decision paralysis.
Considering how cybersecurity is constantly changing, it’s essential to use the most efficient tools available. AI-driven pen testing outperforms traditional methods in nearly every aspect, from efficiency to adaptability and coverage. Human oversight adds the creativity needed to enhance its effectiveness.
Terra Security offers the first-ever agentic-AI platform for offensive security, focused on web application pen-testing and red-teaming. Our penetration testing goes beyond automation, incorporating a deep understanding of each application’s business logic, use cases, customers, and risks and adapting in real-time for tailor-made attacks. It also leverages an out-of-the-box human-in-the-loop mechanism for expert accuracy validation.
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