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Our Thoughts on the Use of AI and Automation in Offensive Security

The Rise of AI in Red Teaming: Opportunity or Overreach?

When CSO Online reported that the top red teamer in the U.S. is now an AI bot, surpassing dozens of human hackers on HackerOne, it sent a jolt through the cybersecurity community. The tool, named Xbow, isn’t just good; it’s prolific, uncovering over a thousand vulnerabilities in 90 days, including critical and zero-day findings. It’s fast, tireless, and alarmingly effective.

But what does this mean for red teams and penetration testers across the industry? Are we witnessing the beginning of a fully autonomous offensive security era, or is this just the next evolution in tooling, one that still relies heavily on human expertise to guide, interpret, and act?

At TrollEye Security, we’ve spent years on the front lines of offensive security. And while we’re excited about the potential of AI to augment red teaming and penetration testing, we’re also clear-eyed about its limits. In this week’s article, we’ll explore where AI shines, where human red teamers are still irreplaceable, and how to strike the right balance between speed, scale, and strategic depth.

Table of Contents

Striking the Right Balance Between AI and Human Expertise in Red Teaming
Where to Implement AI in Red Teaming
Five Ways to Make AI Work in Real Red Teaming Environments
Our Final Thoughts - Augmented, Not Automated

Striking the Right Balance Between AI and Human Expertise in Red Teaming

The rise of Xbow has opened new doors, but it’s also raised a critical question: what role should AI actually play in red teaming and penetration testing?

On one hand, AI tools are faster, more scalable, and increasingly capable of identifying complex vulnerabilities across large environments. On the other hand, red teaming isn’t just about automation; it’s about adversarial thinking, business context, and strategic execution.

Where AI Excels in Offensive Security

AI’s growing role in red teaming isn’t hype; it’s already proving itself in the wild. Tools like Xbow demonstrate how machine learning and automation can dramatically reduce the time required to identify vulnerabilities, chain exploits, and even discover zero-days. When configured properly, AI systems can:

  • Rapidly scan massive environments across IP ranges, web apps, and cloud services, cutting initial reconnaissance time from days to minutes.
  • Generate and validate exploit chains using logic-based workflows that mimic attacker behavior.
  • Enumerate potential paths to impact with machine-speed accuracy, highlighting low-hanging fruit and misconfigurations that may otherwise be overlooked.

These capabilities give red teams a serious boost. AI can handle the heavy lifting of repetitive tasks, running scanners, parsing logs, brute-forcing credential combos, and enumerating endpoints, freeing human operators to focus on higher-order analysis.

But speed and breadth aren’t the same as strategy, and that’s where human expertise remains critical.

Where the Human Touch Still Matters

Red teaming isn’t just about finding flaws; it’s about thinking like an adversary with a purpose. While AI can map a network and test for known vulnerabilities, it can’t replicate the creativity, judgment, or adaptability that real-world campaigns demand.

  • Strategic Targeting: Humans understand business logic, organizational structures, and supply chain dependencies. That means we can prioritize high-value targets based on real-world risk, not just surface-level exposure.
  • Social Engineering: While AI can generate phishing messages using publicly available data, it lacks the judgment and nuance to craft spear-phishing emails that exploit evolving business situations, interpersonal dynamics, or internal company language. 
  • Physical Intrusion: AI can’t tailgate into a data center, clone an RFID badge, or exploit weak physical access controls. Gaining physical access still requires on-the-ground creativity, improvisation, and risk-aware decision-making that no AI can replicate.
  • Interpreting Impact: A vulnerability scanner may flag dozens of issues, but only a human can contextualize them within an organization’s threat model, separating cosmetic findings from real business risk.

That’s why, even in an AI-augmented world, it’s the human element that transforms a list of vulnerabilities into a meaningful, adversary-simulated engagement.

AI is reshaping the offensive security landscape, but it’s not replacing red teamers. The future isn’t man or machine. It’s man and machine. The most effective teams will be those that combine AI’s speed and coverage with human insight, creativity, and precision. In the end, it’s not about choosing sides, it’s about choosing synergy.

"AI has become a powerful accelerator in red teaming, but it’s not a replacement for human expertise. The real value comes from using AI to automate repetitive tasks, and to augment the humans, so our operators can focus on the things that AI can't do."

Avery Rozar
CEO of TrollEye Security

Where to Implement AI in Red Teaming

When applied thoughtfully, AI isn’t a replacement for red teamers; it’s an amplifier. The key is understanding when to let AI take the lead, and where to insert human judgment for maximum effect.

In a mature offensive security program, the best results come from blending automation with human expertise across the engagement lifecycle:

Phase Optimal Role of AI Essential Human Input
Reconnaissance Fast scanning, fingerprinting, OSINT collection. Target prioritization based on business risk.
Discovery Rapid enumeration, exploit generation. Filtering false positives, chaining meaningful paths.
Exploitation Proof-of-concept development, automation chaining. Safe execution, custom payloads, lateral movement.
Assessment Auto-generated severity scoring. Real-world risk analysis, stakeholder alignment.
Reporting & Debriefing Data visualization, metrics. Storytelling, remediation strategy, board-level communication.

Rather than replace workflows, AI should be seen as a set of accelerators. For example, your AI tools might identify dozens of potentially exploitable paths, but your red team chooses the ones most likely to achieve real-world objectives like data exfiltration or domain compromise.

Equally important is transparency and control. Every AI-generated finding should be reviewed before execution, particularly in production or sensitive environments. Guardrails, review gates, and clearly defined escalation paths help ensure AI-driven insights don’t introduce unnecessary risk.

Five Ways to Make AI Work in Real Red Teaming Environments

For all its potential, AI isn’t a plug-and-play solution. It needs to be integrated thoughtfully into red teaming workflows to enhance, not replace, capabilities. That starts with clearly defining what AI should handle and where humans remain in control.

Here’s a practical approach to adopting AI in a red teaming or penetration testing program:

#1. Start with Augmentation, Not Automation

AI works best when supporting red teamers, not running entire engagements alone. It’s ideal for tasks like reconnaissance, subdomain enumeration, and drafting exploit code, jobs that benefit from speed and scale. But every AI-generated result must be reviewed by a human before use to ensure technical accuracy and business relevance.

#2. Build Review Gates Into Every Phase

As AI outputs become more influential, structured review points are essential. Whether AI flags vulnerabilities or generates attack chains, human analysts must validate the findings and decide if they align with the engagement’s goals. These checkpoints prevent false positives and ensure responsible execution.

#3. Create Mixed Playbooks

The strongest workflows combine AI-driven discovery with human-led decision-making. AI can surface initial attack paths, but red teamers evaluate which are worth pursuing, adjust tactics, and execute based on real-world context. Mixed playbooks help teams scale efficiently without losing strategic focus.

#4. Train Your Team on Prompting and Oversight

Red teamers need to be fluent in working with AI, starting with effective prompting and critical review of outputs. AI may accelerate findings, but human judgment is still required to verify their accuracy, applicability, and risk. Teams must be trained not just to use AI, but to challenge it.

#5. Align with Defensive Strategy

AI-assisted red teaming should feed directly into defense. Findings should support purple teaming, patch prioritization, and threat detection efforts. When offensive insights inform defensive improvements in near real time, organizations gain a meaningful edge in both speed and resilience.

Bringing AI into red teaming isn’t about replacing human operators; it’s about leveling up their capabilities. When integrated thoughtfully, AI can dramatically accelerate the early stages of testing and enhance operational depth. But without proper structure, oversight, and human interpretation, it can just as easily create noise or even risk.

The organizations seeing the most value are those building hybrid workflows, where AI handles the scale, and humans drive the strategy. It’s not just about using new tools; it’s about reshaping how red teams think, plan, and execute in a world where attackers are moving at machine speed.

"Xbow shows what we’ve all suspected: AI can chain exploits, sometimes. But chaining two bugs isn’t the same as navigating a living network, adapting on the fly, or recognizing when a ‘low’ severity vuln is the key to the kingdom. Until AI learns to think like a threat actor, not just scan like one, the most dangerous and important element in security remains human, both attacker and defender respectively."

John Andrew
Director, Security Solutions and Partnerships at TrollEye Security

Our Final Thoughts - Augmented, Not Automated

The future of red teaming won’t be defined by AI alone, but by how effectively human experts use it. At TrollEye Security, we see AI not as a replacement, but as a force multiplier for experienced operators. From the start, we’ve used automation heavily to work faster, dig deeper, and uncover more, but it’s the human element that gives each assessment meaning, context, and real-world impact.

Our services from PTaaS to Red Teaming Assessments blend automation where it’s most effective with skilled operators who understand your environment, your business risks, and how real adversaries think. We look forward to using AI to assist human-led testing and assessments to ensure every engagement reflects the way attackers operate today, and how they’ll evolve tomorrow.

AI may be fast, but smart and strategic adaptivity is indispensable when it comes to testing your defenses against real-world threats.

Learn More About Our Red Teaming Assessments

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