The rise of generative AI has reshaped the economics of cybercrime. What once required technical sophistication and painstaking effort can now be achieved with freely available tools: phishing kits crafted to perfection, BEC emails threaded seamlessly into prior conversations, malware that evolves autonomously with every execution. The result is an arms race in which adversaries scale their operations with unprecedented speed, while defenders risk being overwhelmed by volume, variety, and velocity.

Nearly 98.4% of security leaders report that bad actors are already using AI to attack their organizations. Against this new adversary, traditional defenses cannot hold the line. Static rules, keyword filters, and reactive incident response can’t match an adversary whose greatest strength is adaptability. The future of enterprise security now belongs to organizations that can leverage AI as their greatest defensive advantage.

For decades, detection has been built on known indicators of compromise. But in an AI-driven threat landscape, the telltale signs of fraud are vanishing. Polished phishing emails contain no obvious red flags. Vendor compromise arrives through verified domains and accurate thread history. Even insider threats can operate below the threshold of traditional monitoring.

Defensive AI represents a fundamental shift. Instead of relying on signatures or heuristics, it models human and organizational behavior: how employees correspond, how departments interact, how vendors exchange information. By establishing detailed baselines of “normal,” Defensive AI can surface the deviations that reveal risk: a change in tone, an unusual login pattern, or a request that diverges from long-established workflows. This shift restores the advantage to defenders, allowing them to act before novel attacks escalate into breaches.

The practical power of this approach is best seen in real-world scenarios. Business email compromise remains one of the costliest forms of cybercrime, and generative AI has made it more convincing than ever. Attackers now use stolen email threads to generate responses indistinguishable from the legitimate sender. Traditional controls like SPF checks, lexical analysis, and domain reputation cannot keep pace. Defensive AI, however, can detect when the cadence, tone, or timing of communication diverges from the established norm, surfacing risk even in flawless prose.

The same applies inside the security operations center. Alert fatigue has long been a structural challenge, with analysts forced to triage endless queues of low-value reports. That challenge is only intensifying as the attack surface grows, which is why 100% of surveyed security professionals recently identified implementing AI in the SOC as their top business objective.

By automating the analysis of user-reported messages and cross-referencing them against behavioral baselines, Defensive AI can reduce manual review by more than 90%. This does more than increase efficiency; it allows security teams to prioritize response over triage, focusing human judgment where it matters most.

Supply chain compromise offers another example. Vendor accounts, once breached, can send authentic-seeming invoices and requests that bypass legacy controls. Because Defensive AI tracks long-term patterns of interaction—when vendors usually send invoices, how they phrase communications, which accounts they typically use—it can flag the subtle deviations that indicate compromise, even when authentication records appear clean.

Operationalizing Defensive AI requires more than deploying a new toolset. It demands a deliberate strategy, one that aligns technology, operations, and organizational trust. The new Abnormal CISO Guide to Defensive AI outlines five principles for building such a foundation: precision and adaptability, API-level signal fidelity, automation aligned to risk, cross-functional collaboration, and continuous model improvement. Together, they form a blueprint for embedding AI into the fabric of defense, ensuring it evolves in step with both enterprise complexity and adversary innovation.

Crucially, this strategy is not about replacing human expertise but augmenting it. By automating what machines do best—detecting anomalies at scale, executing repeatable workflows—security teams can reserve human judgment for edge cases and high-impact decisions. In an environment defined by speed, scale, and subtlety, that combination is essential.

AI has already become a defining force in cybercrime. The question for CISOs is whether it becomes an equally defining force in their defense strategy. Those who embrace intent-focused, behaviorally grounded Defensive AI will be positioned to counter adversaries at machine speed and scale. Those who remain tied to reactive, rules-based approaches will find themselves defending with yesterday’s tools against tomorrow’s threats.

The Abnormal CISO Guide to Defensive AI provides a framework for navigating this moment: how to shift from chasing indicators to understanding intent, from triaging noise to acting with precision, and from reacting after the fact to anticipating risk before it escalates. In the age of good AI versus bad AI, the advantage belongs to those who operationalize the former.

To explore the advantages of an AI-native defensive architecture, read the full report.