Virtual Patching Should Not Rely on Human Effort: Why AI-Driven Microsegmentation Is the Only Way to Make It Sustainable

In our previous article, we discussed why microsegmentation is the most practical form of Virtual Patching when devices cannot be updated. However, in real-world environments, an even more critical question quickly emerges: Virtual Patching may be possible — but can it be sustained over time? In most cases, the answer comes down to a single bottleneck: human maintenance cost.

Three Real-World Challenges of Traditional Virtual Patching

In many enterprise and device environments, Virtual Patching does not fail because it is technically impossible —

it fails because it cannot be maintained at scale.

1. Rules Depend Heavily on Manual Configuration

Traditional Virtual Patching requires:

Manual analysis of device communication behavior

Hand-crafted firewall or isolation rules

Continuous decisions about which connections should be allowed or blocked

This may work in a lab, but it does not scale in real production environments.

2. Allowlists Become Obsolete Very Quickly

Devices are not static. They constantly change due to:

• Scheduled maintenance

• Software or firmware updates

• Process adjustments

• New systems or devices being added

Any of these changes can instantly invalidate an allowlist —

resulting either in false blocks that disrupt operations, or in overly permissive rules that defeat the purpose of protection.

3. Operational Cost Grows Exponentially Over Time

In practice:

• A single device may have 20–200 legitimate communication flows

• A production line may contain dozens or hundreds of devices

• Every adjustment requires skilled personnel to reassess the rules

As a result, Virtual Patching often becomes a one-time project, not a sustainable security mechanism.

The Problem Is Not Technology — It’s “Who Manages It?”

This is one of the most common observations Janus makes when helping customers improve cybersecurity governance:

Adding another firewall is not the problem.

The real question is: who has the time, the expertise, and the willingness to maintain it long term?

Especially in product and production environments:

• Dedicated security staff are rarely on site

• IT teams are already overloaded

• Any device configuration change may impact validation and compliance

Under these conditions, human-driven Virtual Patching is simply not realistic.

Why AI Is the Key to Making Virtual Patching Work

For Virtual Patching to become a long-term defense strategy, it must meet three requirements:

1. Understand what “normal behavior” looks like for each device

2. Automatically adapt to environmental changes

3. Avoid adding ongoing operational burden

This is exactly where AI-driven microsegmentation delivers its value.

How AI-Driven Microsegmentation Solves the Pain Points

Automated Learning Instead of Human Guesswork

Rather than relying on manually defined rules, AI:

• Observes device communication over time

• Builds behavioral baselines

• Automatically identifies which connections are necessary and legitimate

Allowlists shift from manual configuration to behavioral modeling.

Continuous Updates Instead of Constant Rework

When device behavior changes due to maintenance, updates, or process adjustments:

• AI relearns the new patterns

• Policies are updated dynamically

• No repeated manual intervention is required

Virtual Patching becomes resilient and continuous.

Real-Time Blocking of Unknown Behavior

Any communication not included in the behavioral model:

1. Is blocked immediately

2. Cannot be used for lateral movement

3. Does not disrupt existing legitimate operations

This is the core objective of Virtual Patching.

Janus in Practice: Turning Virtual Patching into an Automated Defense Layer

Janus netKeeper was designed to transform Virtual Patching

from a high-maintenance project into a continuously operating security mechanism.

Key advantages of Janus AI-driven microsegmentation:

• OS-independent

• No agent required

• No changes to device configuration

• AI-based learning and automatic allowlist updates

• Full support for EOS / EOL devices

  
  

This allows Virtual Patching to be deployed in:

1. Semiconductor manufacturing equipment

2. Medical devices

3. Industrial control systems and critical infrastructure

— without compromising stability, validation, or compliance.

Conclusion: Virtual Patching Without AI Is Not Sustainable

In the world of critical devices and product cybersecurity, the question is no longer “Can we implement Virtual Patching?” but rather “Can we sustain it?” When human effort becomes the limiting factor, AI automation is no longer optional — it is essential. This is why AI-driven microsegmentation is increasingly becoming the most practical and deployable Virtual Patching approach behind:

• SEMI E187

• FDA Cybersecurity Guidance

• EU Cyber Resilience Act (CRA)