When Technology Resets the Playing Field
In 2015 I founded a cybersecurity testing software company with the belief that automated penetration testing was not only possible, but necessary. At the time, the idea was often met with skepticism, but today, with 1200+ of enterprise customers and thousands of users, that vision has proven itself. But I also know that what we've built so far is only the foundation of what comes next.
We are now witnessing an inflection point with AI in cybersecurity testing that is going to rewrite the rules of what's possible. You might not see the change in a month's time, but in five years the domain is going to be unrecognizable.
As the CTO of Pentera, I have a vision for the company: one where any security threat scenario you can imagine, you can test with the speed and intelligence only AI can provide. We have already started to implement the individual pieces of this reality into our platform. This article portrays the full vision I have for Pentera in years to come.
AI isn't just another optimization layer for red team tools or security dashboards. It represents a change across the entire lifecycle of adversarial testing. It changes how payloads are created, how tests are executed, and how findings are interpreted. It is redefining what our automated security validation platform can do. Like your cellphone's touchscreen revolution, AI will become the intuitive interface, the engine behind execution, and the translator that turns raw data into decisions.
At Pentera AI is transforming every layer of adversarial testing.
Vibe Red Teaming
Picture this. You're a CISO responsible for protecting a hybrid environment: Active Directory on-prem, production apps in Azure, and a vibrant dev team working across containers and SaaS.
You've just learned that a contractor's credentials were accidentally exposed in a GitHub repo. What you want to know isn't buried in a CVE database or a threat feed, you need to test if that specific access could lead to real damage.
So, you open Pentera and simply say:
"Check if the credentials john.smith@company.io can be used to access the finance database in production."
No scripts. No workflows. No playbooks.
In seconds, the platform understands your intent, scopes the environment, builds an attack plan, and emulates the adversary, safely and surgically. It doesn't stop there.
It adapts mid-test if your defenses react. It bypasses detection where possible, pauses when needed, and reevaluates the path based on live evidence.
And when it's done?
You get a summary tailored for you; not a dump of raw data. Executives receive a high-level risk briefing. Your SOC gets the logs and findings. Your cloud team gets a remediation path.
That's Vibe Red Teaming: where security validation becomes conversational, intelligent, and instantly actionable.
It gets better - picture this as well:
Imagine that from any security application or agent, for example your SOC you want to test for acceptance of your new Cloud environment. Alternatively imagine that your devops team would like to roll your new LLM application model into production.
Those management applications, soon to turn agentic, will call the Pentera Attack-testing API and execute those tests as part of their workflow, assuring that any and every action in your infrastructure is inherently secure as from its inception.
That's a callable testing sub-agent: where any security application and any script can call on security validation operations from within and verify the efficacy and correctness of security controls on the fly.
Transforming Every Layer of Adversarial Testing
To bring this future to life, we're reimagining the adversarial testing lifecycle around intelligence, infusing AI into every layer of how pentesting and red-teaming exercises are imagined, executed, adapted, and understood. These pillars form the foundation of our vision for a smarter, more intuitive, more human form of security validation.
1. Agenting the Product: The End of Clicks, the Rise of Conversation
In the future, you won't build tests in a template; you'll drive them in natural language. And as the test runs, you won't sit back and wait for results, you'll shape what happens next.
"Launch an access attempt from the contractor-okta identity group. Check if any accounts in that group can access file shares on 10.10.22.0/24. If access is granted, escalate privileges and attempt credential extraction. If any domain admin credentials are captured, pivot toward prod-db-finance."
And once the test is in motion, you keep steering:
"Pause lateral movement. Focus only on privilege escalation paths from Workstation-203."
"Re-run credential harvesting using memory scraping instead of LSASS injection."
"Drop all actions targeting dev subnets, this scenario is finance only."
This is Vibe Red Teaming in action:
No rigid workflows. No clicking through trees of options. No translation between human thought and test logic.
You define the scenario. You direct the flow. You adapt the path. The test becomes an extension of your intent, and your imagination as a tester. Instantly you have the power of red-teaming at your fingertips. Work is already underway to bring this experience to life, starting with early agentic capabilities that act on natural language input to give you more control over your testing in real-time.
2. API-First Intelligence: Unlocking Granular Control of the Attack
We are building an API-first foundation for adversarial testing. Every attack capability - such as credential harvesting, lateral movement, or privilege escalation - will be exposed as an individual backend function. This allows AI to access and activate techniques directly, without depending on the user interface or predefined workflows.
This architecture gives AI the flexibility to engage only what is relevant to the current scenario. It can call specific capabilities in response to what it observes, apply them with precision, and adjust based on the environment in real time.
An API-first model also accelerates development. As soon as a new capability is available in the backend, AI can use it. It knows how to invoke the function, interpret the output, and apply the result as part of the test. There is no need to wait for the UI to catch up.
This shift enables faster iteration, greater adaptability, and more efficient use of every new capability. AI gains the freedom to act with context and control, activating only what is needed, exactly when it is needed.
3. AI for Web Testing: The Web Surface, Weaponized
The impact of AI becomes even more visible when you look at how it shapes common web attack techniques. It doesn't necessarily invent new methods. It enhances them by applying real context.
Pentera has already introduced AI-based web attack surface testing into the platform, including AI-driven payload generation, adaptive testing logic, and deeper system awareness. These capabilities allow the platform to emulate attacker behavior with more precision, speed, and environmental sensitivity than was previously possible.
In the future, AI will make this surface testable in ways that aren't practical today. When new threat intelligence emerges, the platform will generate relevant payloads and apply them as soon as it encounters a matching system or opportunity.
AI will also transform how sensitive data is discovered and used. It will parse terabytes of files, scripts, and databases, not with rigid patterns, but with the awareness of what an attacker is looking for—credentials, tokens, API keys, session identifiers, environment variables, and configuration secrets. At the same time, it will recognize the type of system it is interacting with and determine how that system typically behaves. This context allows AI to apply what it finds with precision. Credentials will be tested against relevant login flows. Tokens and session artifacts will be injected where they matter. Each step of the test will advance with intent, shaped by an understanding of both the environment and the opportunity within it.
Language, structure, and regional variation have often made meaningful testing difficult or even impossible. AI already enables Pentera to remove that barrier. The platform interprets interface logic across languages and regional conventions without the need to rewrite flows or localize scripts. It recognizes intent and adapts accordingly.
This is the direction we're building toward. A system that uses intelligence to emulate threats with precision and helps you understand where to focus, what to fix, and how to secure your environments with confidence.
4. Validating the LLM Attack surface
AI infrastructure is becoming a core part of how organizations operate. Large language models (LLMs) process user input, store memory, connect to external tools, and influence decisions across environments. These systems often carry broad permissions and implicit trust, making them a high-value target for attackers.
The attack surface is growing. Prompt injection, data leakage, context poisoning, and hidden control flows are already being exploited. As LLMs are embedded into more workflows, attackers are learning how to manipulate them, extract data, and redirect behavior in ways that evade traditional detection.
Pentera's role is to ensure you can close that gap.
We will engage with LLMs through real-world inputs, workflows, and integrations designed to surface misuse. When a model produces an output that can be exploited, the test will continue with intent. That output will be used to gain access, move laterally, escalate privileges, or trigger actions in connected systems. The objective is to demonstrate how a compromised model can lead to meaningful impact across the environment.
This is not just about hardening the model. It's about validating the security of the entire system around it. Pentera will give security teams a clear view into how AI infrastructure can be exploited and where they present a risk to the organization. The result is confidence that your AI-enabled systems are not just operational, but secured by design.
5. AI Insights: A Report That Speaks to You
Every test ends with a question: What does this mean for me?
We've already started answering that with AI-powered reporting available in the platform today. It surfaces key exposure trends, highlights remediation priorities, and provides security teams with a clearer view of how their posture is evolving over time. But that is just the foundation.
The vision we are building goes further. AI won't just summarize results. It will understand who is reading, why it matters to them, and how to deliver that insight in the most useful way.
- A security leader sees posture trends across quarters, with risk benchmarks tied to business objectives.
- An engineer gets clear, actionable findings - no fluff, no digging.
- And a boardroom gets a one-page readout that connects security exposure to operational continuity.
And the breakthrough is not just in content. It is in communication. The IT team in Mexico sees the report in Spanish. The regional lead in France reads it in French. No translation delays. No loss of meaning. No need to filter the information through someone else.
The report adapts. It clarifies. It prioritizes. It speaks to your role, your focus, your language. It's not documentation. It's insight delivered like it was written just for you, because it was.
6. AI Support: Testing Without Roadblocks
AI will reshape the support experience by reducing friction at every step - from answering common questions to resolving complex technical issues faster.
A conversational chatbot will help users get unstuck in the moment. It will answer straightforward questions about platform usage, test setup, findings navigation, and general how-to guidance. This reduces reliance on documentation or human intervention for common tasks, giving users immediate clarity when they need it.
For more involved issues, AI will take on a much deeper role behind the scenes. Instead of waiting for a ticket to move through multiple support tiers, users will upload logs, screenshots, or error details directly into the support flow. AI will analyze the input, identify known patterns, and generate suggested resolutions automatically. It will determine whether the issue is usage-related, a known product behavior, or a likely bug - and escalate it only when needed, with full context already attached.
The outcome is faster resolution, fewer back-and-forth cycles, and a shift in the human role - from triaging every request to reviewing and finalizing solutions. Customers spend less time blocked, and more time moving forward.
Conclusion: From Test to Transformation
Vibe Red Teaming is a new experience in security testing. It doesn't start with configuration or scripting. It starts with intent. You describe what you want to validate, and the platform translates that into action.
AI makes that possible. It turns ideas into tests, adapts in real time, and reflects the conditions of your environment as they evolve. You're not building scenarios from templates. You're directing real validation, on your terms.
Built on the foundation of Pentera's safe-by-design attack techniques, every action is controlled and built to avoid disruption, so teams can test aggressively without ever putting production at risk.
This is the foundation for a new model. Testing becomes continuous, expressive, and part of how security teams operate every day. The barrier to action disappears. Testing keeps pace with the threat.
We're already building toward that future now.
Note: This article was written by Dr. Arik Liberzon, Founder & CTO of Pentera.
