How to Use Microsegmentation and AI to Stop Lateral Movement

By seobotai | February 9, 2026

How Microsegmentation and AI Can Transform Cybersecurity: Insights from Black Hat 2025

The cybersecurity landscape is evolving at an unprecedented pace, with attackers leveraging sophisticated techniques and defenders racing to keep up. At Black Hat 2025, Chris Bame, Field CTO of Zero Networks, discussed the pivotal role of microsegmentation and AI in stopping lateral movement – a critical concern for cybersecurity teams worldwide. Moderated by Jackie Macguire, the conversation explored practical applications, challenges, and the future of these technologies. Here’s a deep dive into the key insights shared during the session.

Understanding Microsegmentation: A Security Game-Changer

Microsegmentation is a concept that’s gaining steady traction in the cybersecurity world. Bame explained it with a simple analogy: imagine visiting a bank. Just because you have access to the bank’s lobby doesn’t mean you can freely access the vault or individual safety deposit boxes. Multiple layers of authentication are applied at each step to ensure that access is limited to only those authorized.

In cybersecurity terms, microsegmentation works similarly. It creates fine-grained access controls around specific assets, ensuring that users, devices, or applications can only interact with what they’re explicitly authorized to access. This strategy minimizes the risk of attackers moving laterally within an environment once they’ve breached a single point.

"Microsegmentation enables verification at every layer", Bame emphasized. "It allows segmentation down to the asset level, even in complex environments like the cloud."

As organizations increasingly transition to cloud infrastructures, the importance of microsegmentation grows. The flexibility of platforms like AWS to spin up new services quickly can lead to shadow IT – unapproved or unmonitored systems that inadvertently create vulnerabilities. Microsegmentation provides a framework to secure cloud environments by enforcing strict access policies, even for these ad hoc resources.

The Role of AI: Advantage or Obstacle?

Artificial intelligence (AI) is often regarded as the silver bullet for modern cybersecurity challenges, but Bame offered a more nuanced perspective. While AI excels in certain domains, such as summarizing large datasets or providing guidance, its application in detecting and preventing lateral movement remains limited.

One of the key issues, according to Bame, is that attackers are also leveraging AI to exploit vulnerabilities. Moreover, AI often suffers from false positives or "hallucinations" – situations where it generates unreliable or incorrect results. This makes organizations hesitant to rely solely on AI for critical security functions.

"AI can summarize massive amounts of data and provide direction, but it’s not 100% accurate. When lives or businesses are on the line, you need more than probabilistic guesses", Bame explained.

Instead, Bame advocated for combining automation with microsegmentation. Automation can reduce the burden on security teams by learning from user behavior and implementing policies in real time, while microsegmentation ensures that access remains tightly controlled.

Why AI Struggles with Lateral Movement Detection

Detecting lateral movement – the process by which a threat actor moves within an environment to access sensitive systems – is one of the most challenging aspects of cybersecurity. Despite AI’s promise, it hasn’t yet delivered groundbreaking solutions in this area. Bame outlined several reasons for this:

Complexity of Modern Environments: Enterprise environments are sprawling and diverse, with multiple tools and platforms. AI struggles to adapt and contextualize all these variables in real time.
Dynamic Attack Strategies: Attackers regularly update their techniques, leveraging AI themselves to bypass defenses.
Accuracy Trade-offs: Organizations cannot afford disruptions caused by false positives. For example, a bank cannot tolerate AI making mistakes that interrupt millions of dollars in transactions.

However, AI has proven effective in summarization and guidance roles. For example, it can analyze vast amounts of data to highlight potential vulnerabilities or suggest next steps for incident response. In controlled scenarios, generative AI can even provide practical recommendations.

"AI is useful for summarization and guidance, but true autonomous action remains a challenge", said Bame.

The Future of AI and Microsegmentation Integration

Despite its limitations, AI has clear potential to enhance microsegmentation over time. By analyzing patterns and providing actionable insights, AI could one day develop policies that adapt dynamically to network changes.

Bame suggested a future where AI-driven systems could create and enforce microsegmentation policies autonomously, reducing the need for manual intervention. However, this vision requires overcoming key hurdles, such as ensuring transparency and traceability in AI decision-making.

"Any platform using AI must explain its actions clearly. If it’s a black box, I don’t trust it", Bame cautioned.

For now, the focus should be on balancing AI’s capabilities with robust, human-driven security frameworks. Tools need to empower cybersecurity teams rather than create new challenges or introduce friction into workflows.

Practical Advice for CISOs: Evaluating AI and Microsegmentation Solutions

For CISOs and other security leaders, navigating the crowded market of AI-powered tools can be daunting. Bame offered the following guidelines to separate hype from practicality:

Demand Transparency: Avoid solutions that operate as "black boxes." Ensure the tool explains its actions and provides an audit trail.
Focus on Business Impact: Evaluate whether the solution reduces friction and aligns with your operational goals. Tools that disrupt business continuity can create more harm than good.
Prioritize Ease of Implementation: Microsegmentation tools should integrate seamlessly into existing infrastructure without requiring extensive reconfigurations.
Long-Term Usability: Choose platforms designed for continuous learning and adaptability. The tool should grow with your organization’s needs, not become obsolete after initial deployment.

By leveraging these principles, CISOs can build a resilient security strategy that combines the strengths of AI and microsegmentation.

Key Takeaways

Microsegmentation enhances security by restricting access to specific assets, minimizing the risk of lateral movement within networks.
AI excels in summarization and guidance, but struggles with accuracy and autonomous decision-making in high-stakes scenarios.
Attackers are increasingly using AI, making it essential for defenders to combine automation with robust security frameworks.
Solutions that provide transparency, minimize friction, and integrate seamlessly into existing infrastructure are more likely to succeed.
The future of AI in cybersecurity lies in autonomous policy creation and enforcement, but this requires overcoming current limitations in accuracy and traceability.

Conclusion

The discussion at Black Hat 2025 underscored that while microsegmentation and AI are powerful tools, they are not standalone solutions. Effective cybersecurity requires a layered approach that incorporates automation, transparency, and human oversight. By focusing on practical applications and avoiding overhyped solutions, organizations can stay ahead of increasingly sophisticated threats.

As the cybersecurity community continues to push boundaries, integrating AI and microsegmentation may well define the next era of defense strategies. For now, the key is to strike the right balance – empowering teams to mitigate risks efficiently without compromising on operational stability.

Source: "Hard Truths About AI in Cybersecurity Reveal Truly Hardened Defenses – Chris Boehm" – CyberRisk TV, YouTube, Aug 5, 2025 – https://www.youtube.com/watch?v=05TFkpLK36s