Tech Explained: Traditional cyber defenses cannot match AI-powered threats in Simple Terms

New research argues that cybersecurity must undergo a paradigm shift, moving away from prevention-first thinking toward systems that can reason, adapt, and recover under continuous attack.

In a study titled Agentic AI for Cyber Resilience: A New Security Paradigm and Its System-Theoretic Foundations, published on arXiv, researchers outline a comprehensive framework for redesigning cybersecurity around autonomous, goal-directed AI agents. The paper positions agentic AI not as a support tool for human analysts, but as an active participant in cyber defense, capable of sensing threats, making strategic decisions, executing responses, and learning over time in adversarial environments.

Why traditional cybersecurity models are failing

The study traces the historical evolution of cybersecurity across five distinct paradigms. Early approaches relied on ad hoc protections and isolated controls. These gave way to perimeter-based defenses, followed by risk management frameworks, compliance-driven security, and more recently, AI-assisted detection and response. Each stage improved efficiency and scale, but all shared a common assumption: that threats could be identified, classified, and mitigated through predefined rules or statistical patterns.

According to the authors, that assumption no longer holds. Modern attackers increasingly use AI-driven tools that can plan multi-step campaigns, adapt tactics in real time, and exploit contextual weaknesses rather than fixed vulnerabilities. Large language models enable attackers to automate reconnaissance, generate convincing social engineering content, and coordinate actions across multiple systems without constant human input.

Traditional security tools, even those augmented with machine learning, remain largely reactive. They depend on historical data, known signatures, or static playbooks. When confronted with novel or adaptive threats, these systems either fail silently or escalate to human analysts, creating bottlenecks that attackers can exploit.

According to the study, cybersecurity has reached a point where human-centered workflows alone cannot sustain defense at machine speed. Detection delays, alert fatigue, and fragmented tooling leave organizations exposed during the most critical moments of an attack. In this environment, preventing all breaches is no longer realistic. Instead, the authors contend that security systems must be designed to expect failure and respond intelligently when it occurs.

Agentic AI as a foundation for cyber resilience

Under the hood, the proposed paradigm is agentic AI, defined as artificial intelligence systems that operate with goals, memory, reasoning capability, and autonomy within defined constraints. Unlike traditional automation scripts or classification models, agentic systems maintain an internal representation of the environment, track long-term objectives, and adjust behavior based on feedback.

The paper outlines a system-level architecture for agentic cyber defense built around a continuous sense–reason–act–learn loop. Sensors collect data from networks, endpoints, and cyber–physical systems. A reasoning core, often powered by large language models, interprets this data in context rather than as isolated events. Memory modules store both short-term observations and long-term patterns, enabling the system to recognize evolving campaigns rather than discrete incidents.

Based on this contextual understanding, agentic AI selects actions such as containment, deception, recovery, or coordination with other agents. Crucially, these actions are not hard-coded responses but strategic choices evaluated against system goals, such as maintaining availability, protecting critical assets, or minimizing operational disruption. After execution, the system observes the outcome and updates its internal models, refining future behavior.

Agentic AI does not eliminate the role of humans. Instead, it redistributes responsibility. Humans define high-level objectives, constraints, and ethical boundaries, while agents handle tactical execution and continuous adaptation. Human-in-the-loop oversight remains essential for accountability, escalation, and strategic governance, but routine decision-making is delegated to machines capable of operating at cyber scale.

This shift enables a move from security as static protection to security as dynamic resilience. Rather than focusing solely on stopping attacks at the perimeter, agentic systems aim to preserve core functionality, contain damage, and recover quickly even when defenses are breached.

Cyber conflict as a strategic interaction

Both attackers and defenders are modeled as learning entities that observe each other’s actions and adjust strategies accordingly. This perspective reflects the reality of modern cyber conflict, where attacks unfold over weeks or months and involve continuous probing, deception, and countermeasures.

By applying game theory, the authors provide a formal language for designing agentic systems. Decisions about when to automate, when to escalate to humans, how much information to reveal, and how aggressively to respond can all be framed as strategic choices within an adversarial game. The goal is not to achieve a static optimal defense, but to maintain equilibrium under persistent pressure.

This approach also helps address a central risk of autonomous systems: unintended escalation. Poorly designed automation can overreact, disrupt operations, or provoke attackers into more aggressive behavior. Game-theoretic design allows developers to reason about incentives, stability, and long-term outcomes, reducing the likelihood of runaway responses.

The study highlights that agentic AI changes the symmetry of cyber conflict. Attackers already benefit from automation and scalability. Defenders, by contrast, often rely on manual intervention and fragmented tooling. Agentic defense narrows this gap by enabling coordinated, autonomous responses that match attacker speed and adaptability.

The framework extends beyond purely digital systems. The authors explicitly address cyber–physical environments, such as power grids, transportation networks, and industrial control systems. In these settings, cyber attacks can produce physical harm, making resilience even more critical. Agentic AI can coordinate responses across digital and physical layers, balancing safety, continuity, and recovery in real time.

Redefining security around resilience

A recurring theme throughout the paper is the rejection of perfect prevention as a viable goal. In complex, interconnected systems, breaches are inevitable. Zero-risk security is neither technically achievable nor economically sustainable. The authors argue that clinging to prevention-centric metrics obscures more meaningful measures of security performance.

Instead, the study proposes resilience as the primary objective. Cyber resilience is defined as the ability to anticipate threats, absorb disruption, maintain essential functions, recover efficiently, and adapt to future attacks. This temporal view recognizes that security is not a binary state but a continuous process unfolding before, during, and after incidents.

Agentic AI supports this shift by enabling proactive preparation, adaptive response, and retrospective learning. Agents can simulate attack scenarios, deploy decoys, and reconfigure systems preemptively. During incidents, they can isolate compromised components, reroute services, and coordinate recovery. Afterward, they can analyze outcomes and update strategies, reducing the impact of similar attacks in the future.

This resilience-oriented model aligns closely with how other safety-critical domains operate, such as aviation or emergency management. The study suggests that cybersecurity must adopt similar principles as digital systems become foundational to economic and social infrastructure.

Implications for organizations and policymakers

The transition to agentic cyber resilience carries significant implications for technology providers, enterprises, and regulators. For organizations, it requires rethinking security architecture, governance, and workforce roles. Investments must shift from isolated tools toward integrated systems capable of coordination and learning.

Data quality and interoperability become critical enablers. Agentic systems rely on timely, accurate information from across the enterprise. Siloed data and incompatible platforms limit their effectiveness. Organizations must also establish clear policies defining acceptable autonomy, escalation thresholds, and accountability mechanisms.

For policymakers, the rise of agentic AI raises questions about safety, oversight, and standardization. Autonomous defense systems operating at machine speed challenge existing regulatory frameworks designed around human decision-making. The study suggests that governance should focus on outcomes, constraints, and transparency rather than attempting to micromanage technical implementation.

The authors also note that the same agentic technologies that enable defense can be used offensively. This dual-use nature reinforces the need for international dialogue and norms governing autonomous cyber operations. Without coordination, an arms race of increasingly autonomous agents could destabilize digital ecosystems.