Preemptive Cybersecurity: AI vs. AI

The Munich Security Conference is fast approaching, and resilience – particularly enterprise resilience – and cybersecurity will be center stage at this year’s event. That’s hardly surprising. Cybersecurity is at an inflection point, with 87% of security leaders now believing that AI is driving increasingly frequent and sophisticated attacks. Yet only 29% feel adequately equipped to counter this threat.

Traditionally, cybersecurity has relied on an intrinsically reactive model, summed up by the key actions “detect, respond, patch, repeat”. But if defense is to master current and future challenges, it must shift to an earlier position in the kill chain.

Why Established Approaches Fail

Established cybersecurity approaches rely on a combination of signature-based detection, static rules, and manual investigation. However, AI is rapidly rendering approaches of this kind obsolete. More specifically, there’s a growing gap between the sophistication of cyberattacks and current defense mechanisms.

Today, automated attacks, such as AI-generated phishing campaigns, can be executed in a matter of seconds. What’s more, AI is enabling malicious actors to radically reduce the time between discovering and exploiting security weaknesses. However, defense teams often still rely heavily on human investigation and manual patching, giving attackers a significant edge.

That’s where preemptive cybersecurity comes in. This approach differs from conventional reactive methods in that it seeks to interrupt the kill chain before an attack reaches a critical stage, such as privilege escalation (unauthorized gaining of higher-level permissions) or data exfiltration (unauthorized transfer or theft of sensitive data).

A New Arms Race: AI Attacks vs. AI Defense

As a result of these developments, cybersecurity has now become an arms race between AI-enabled offense and defense. Today’s attackers leverage AI to automate phishing, create deepfakes, scan for vulnerabilities at scale, and customize exploits.

To counter these threats, defenders must deploy AI to detect attack patterns, predict threat paths using global intelligence, and automate real-time defensive actions. In short, if defense is to succeed, detection and action must go hand in hand.

Effective Detection: AI-Enhanced Monitoring

But what does this new form of AI-based defense look like in practice? Detecting attack patterns generally relies on machine learning (ML) and generative models to dynamically understand “normal” behavior across users, devices, networks, and applications.

This enables the detection of subtle deviations, including anomalous behavior, lateral movement, credential misuse, novel malware, and unexpected access patterns. Thanks to continuous learning, ML-equipped systems of this kind can flag suspicious activity before it escalates into a full-scale incident.

Such is the promise of these systems that 40% of companies have already adopted AI for anomaly detection and to support incident response.

Effective Action: Automated Response Playbooks

It goes without saying that machines can act significantly faster than humans. That’s why organizations are increasingly using automated response playbooks to execute predefined if/then defensive actions, such as the following:

• Locking user accounts and triggering multifactor reauthentication for unusual login attempts

• Isolating areas of software and systems and capturing forensic data in the event of lateral movement behavior

  
  

Insights gained via AI play a pivotal role in driving these playbooks, which are executed through security orchestration, automation and response (SOAR) systems. Alongside automation, adaptability is another key requirement for these systems and is achieved through reinforcement learning and feedback loops.

Mastering the Paradigm Shift in Cybersecurity

Addressing the growing use of AI by attackers entails a significant shift in cybersecurity for organizations. To successfully adapt, it’s imperative that tech leaders focus on the following core areas:

• Predictive threat intelligence 

• Continuous monitoring and behavior analytics 

• Automated response orchestration

• Simulation and red teaming

Let’s take a brief look at each of these: Predictive threat intelligence is all about leveraging global and local data, such as threat feeds, telemetry, and dark web indicators, to forecast emerging threats. Continuous monitoring and behavior analytics serve to establish baselines and detect subtle anomalies that might indicate reconnaissance or lateral movement.

Automated response orchestration involves integrating the automated playbooks discussed above into incident response frameworks. And simulation and red teaming are deployed to mimic AI-driven attacks and ensure that defenses stay ahead of attackers.

Why a Proactive Approach Is Now a Must

As mentioned, reactive cybersecurity models already fall short of today’s requirements. With 65% of IT leaders now stating that their current defenses cannot prevent AI-powered cybercrime, action is urgently needed.

The rapidly evolving nature of cyberattacks underscores the growing need for AI-centric defense frameworks that can simulate attacks and orchestrate defenses. So, it’s no surprise that nearly two-thirds (66%) of organizations plan to increase their cybersecurity investments in 2026.

It’s important to remember that, as a strategic imperative, preemptive cybersecurity reaches far beyond IT, impacting enterprise resilience as a whole.

Want to Take a Deeper Dive?

If you’d like to take a closer look at preemptive cybersecurity, feel free to reach out to me. And if you’d like to share your ideas on the new cyberthreats facing organizations and how best to defend against them, please leave a comment below.