5 steps to match AI cyber defense with advancing agentic threat

It’s time for organizations to fight AI threat with AI cybersecurity tools.

In brief

• 27 seconds is the time it now takes for an AI attack to breach an organization’s defenses.
• Traditional cybersecurity software and processes can no longer match the speed, volume or complexity of AI-driven attacks.
• SecOps leaders should switch models to match agentic AI cybersecurity with advanced AI-led attacks.

Modern cybersecurity software comes down to some important numbers. Start with this: 27 seconds. That’s the new record-breaking speed of modern cybersecurity incidents that organizations now face. At this pace, traditional detection and defense systems simply can’t keep up. Even the most highly optimized response protocols and cybersecurity tools were still built around the idea of human triage, investigations and rapid response – and were never designed for the age of escalating machine-speed attacks. For many cybersecurity leaders, their organizations are at a crucial inflection point. As attackers move with frontier model AI speed, cybersecurity teams are being asked to do more and do it faster – often with stretched budgets, higher alert volumes and talent shortages. Forward thinking cybersecurity leaders are now turning to the kinds of agentic AI technologies that are driving incidents to mount a more sophisticated and attack-appropriate response. For decision makers looking to accelerate defenses and protect their businesses there are five cybersecurity tips to consider in the age of agentic threat.

1. Update defense models for today’s evolved threat

According to the CrowdStrike 2026 Global Threat Report, organizations report that AI-enabled cyber-attacks have risen 89%, but 90% of organizations admit they lack the advanced experience and cybersecurity tools required to counter today’s threats from cloud exploitation to AI-powered attacks. Most are running traditional security operations center (SOC) models that still reflect an earlier era of cybersecurity software where analysts reviewed alerts, investigated incidents and coordinated responses. In the human-led response model this could mean coordinating across security team handovers and shifts. All of that worked when attacks were perhaps more singularly focused, manual deployed by attackers and unfolded over longer periods of time that allowed response coordination. But in today’s world, the level of speed, volume and frequency of complex attacks creates a constant pressure on security teams and organizational expectations for near real-time response.

2. Adopt agentic defense to refocus resources

With machine-speed attacks responses can’t wait for measured human decision making. But autonomous defense can’t come at the expense of appropriate attack assessment. Organizations are shifting away from solely human-driven workflow models toward agentic security platforms. Here AI agents take on high-volume, time-sensitive tasks at machine speed, triaging alerts at the time they occur. Crucial investigative activity occurs across environments in real time, and the agents either take or instantly recommend courses of action based on individual attack criteria and both predefined logic and learned patterns. This model enables SecOps teams to refocus their attention and resources on higher risk activities and decisions as well as managing more complex alert escalations.

3. Automate routine processes to elevate teams

One of the most consistent challenges SecOps leaders raise is how to manage the sheer volume and velocity of alerts. Agentic models help address this by shifting the burden away from manual processes and toward automated orchestration. Instead of analysts constantly reacting in the moment to each alert, routine tasks are handled automatically by SOC agents and investigations happen continuously rather than sequentially. This ability to initiate responses in real time – minutes not hours – has a significant impact on how teams are able to operate. As cybersecurity becomes integral to business continuity and customer trust, SOC agents can reduce the gap between detection and response, improve consistency in how threats are handled and focus SecOps teams crucially on strategic and proactive defense initiatives.

4. Consider technology and operating model equally

It’s easy to focus on the technology behind an agentic security platform. But what often matters just as much is how that technology is operationalized. AI agents are only effective when they’re integrated into end-to-end workflows, designed around real-world use cases and supported by security expertise and governance. This is why many organizations explored managed approaches, where advanced platforms are paired with deep operational and domain experience. In this instance, the SOC goes beyond event monitoring and reaction to continuously evolve with the threat landscape and cybersecurity operations become a source of ongoing insight and operational improvement across the company.

5. Match machine speed detection with human decision making

Since generative AI models first emerged and then advanced quickly, there’s been a unstated undercurrent of concern from organizations that AI models might replace human roles in cybersecurity. But in actuality, what’s happening is very different. The most effective defenses are being intentionally designed around human and machine collaboration. AI-driven agentic SOC manages incidents at speed and increasing scale, matching the advancing sophistication of AI-driven threats. But it’s humans with deep industry experience and key knowledge of business operations and goals that bring the crucial context, add critical judgement and help to ensure accountability for actions taken. It’s a potent combination for many leading organizations offering a new path to regaining control in a risk landscape and threat environment that increasingly favors attackers. The call to action is clear: detection must happen at machine speed and humans must pull the necessary strategy levers to effect the best outcomes.