Cybersecurity’s AI Evolution: From Smart Tools to Autonomous Defenders

Author: Denis Avetisyan

A new analysis charts the rapid progression of artificial intelligence in cybersecurity, examining how systems are moving beyond simple reasoning to fully automated threat response.

This review details a five-generation taxonomy of agentic AI, tracing its development from single large language model reasoners to complex multi-agent systems and autonomous security pipelines.

While cybersecurity teams increasingly seek automation to address escalating threats, relying solely on individual large language models proves insufficient for complex, real-world scenarios. This survey, ‘The Evolution of Agentic AI in Cybersecurity: From Single LLM Reasoners to Multi-Agent Systems and Autonomous Pipelines’, presents a five-generation taxonomy charting the rapid architectural shift from basic LLM helpers to sophisticated multi-agent systems and semi-autonomous investigative pipelines. Our analysis reveals a clear progression in capabilities – reasoning depth, tool use, and reproducibility – alongside evolving risks at each stage of development. As agentic AI becomes more deeply integrated into security operations, how can we best ensure safe, reliable, and accountable autonomous responses to emerging cyber threats?

From Reasoning to Action: The Evolution of Agentic AI

Initial explorations into agentic artificial intelligence centered on the direct application of large language models (LLMs) to solve complex problems, showcasing a compelling ability to generate coherent plans and responses. These early systems demonstrated promise in tasks requiring linguistic reasoning and knowledge recall, suggesting a pathway toward autonomous agents. However, this first generation of agents quickly revealed critical limitations in real-world application; their reasoning, while seemingly sound, proved brittle and prone to errors when faced with novel situations or incomplete information. A core issue was the LLM’s reliance on its pre-trained knowledge, lacking the ability to dynamically adapt or verify information, which resulted in a lack of robustness and difficulty generalizing beyond the scope of its training data. Consequently, these systems often struggled with tasks demanding factual accuracy, practical grounding, or interaction with dynamic environments.

Initial forays into agentic artificial intelligence centered on leveraging the reasoning capabilities of large language models, but these “Gen-1” systems quickly revealed limitations in real-world applicability. While proficient at linguistic tasks, they struggled with actions requiring external information or complex operations beyond text generation. This prompted a shift towards “Gen-2” Tool-Augmented Agents, designed to overcome these hurdles by integrating access to and control over external tools – such as search engines, calculators, or APIs. By offloading specific tasks to specialized instruments, these agents demonstrate enhanced capabilities in problem-solving, data analysis, and autonomous execution, effectively extending the reach of the LLM beyond pure reasoning and into the realm of practical action and dynamic interaction with the external world.

Orchestrating Complexity: The Emergence of Multi-Agent Systems

Gen-3 Multi-Agent Systems represent an evolution in artificial intelligence architecture, moving beyond single large language models (LLMs) to distributed systems comprised of specialized agents. This progression allows for the decomposition of complex tasks into smaller, more manageable sub-tasks, each handled by an agent designed for that specific function. The orchestration of these agents enables improved workflow management, as agents can operate concurrently and pass information between each other, resulting in a more efficient and adaptable system compared to monolithic approaches. This specialization fosters increased reliability and reduces the computational burden on any single model, leading to performance gains in complex operations.

Recent evaluations of Security Operations Center (SOC) workflows demonstrate that Gen-3 Multi-Agent Systems achieve a 150% increase in tasks completed compared to single, monolithic Large Language Models (LLMs). This performance gain is attributed to the ability of multi-agent systems to decompose complex security incidents into smaller, specialized tasks assigned to individual agents. This parallel processing capability significantly reduces overall incident resolution time and increases the throughput of security analysts, allowing for more effective threat detection and response. The measured improvement represents a substantial increase in operational efficiency within SOC environments.

Standardization Through Structure: Schema-Bound Ecosystems

Gen-4 Schema-Bound Ecosystems mitigate the inherent complexities of multi-tool workflows by mandating strict adherence to predefined schemas for all tool interactions. These schemas function as contracts, specifying the expected input and output formats for each tool, thereby reducing ambiguity and potential errors. By enforcing this structural consistency, the ecosystem ensures that tools operate predictably within the larger workflow, simplifying debugging and increasing the overall reliability of automated processes. This approach contrasts with earlier generations of automation which often relied on looser integrations and ad-hoc communication, leading to brittle and difficult-to-maintain systems. The rigid structure inherent in schema-bound systems allows for formal verification and validation of tool interactions, enhancing confidence in the automation’s behavior.

The CACAO framework facilitates the development of standardized cybersecurity automation by providing a mechanism for defining and enforcing schemas on tool interactions. This schema-driven approach ensures that tools receive and transmit data in a predictable format, reducing integration errors and enhancing interoperability. Specifically, CACAO leverages a declarative approach to specify input and output requirements for each tool within an automation workflow. This enables the creation of repeatable processes, as consistent data structures minimize the potential for runtime failures due to unexpected input. Furthermore, the framework supports versioning of these schemas, allowing for controlled evolution of automation pipelines while maintaining backward compatibility and ensuring reproducibility of results across different execution environments.

Schema-bound tool ecosystems enhance reproducibility in agentic AI deployments by strictly defining the input and output parameters for each tool interaction. This enforced structure mitigates risks associated with unpredictable tool behavior and ambiguous data exchange, thereby improving the consistency of results across multiple runs. Furthermore, the predefined schemas contribute to improved safety by limiting the potential for agents to utilize tools in unintended or harmful ways, as any deviation from the specified schema will be rejected, preventing potentially malicious actions or system instability. This constraint on tool usage is crucial for deploying agents in sensitive environments or applications where predictable and secure operation is paramount.

Towards Autonomous Cybersecurity: A Proactive Defense

Gen-5 Autonomous Pipelines signify a pivotal leap in cybersecurity, moving beyond reactive measures to establish fully automated workflows capable of independently identifying, analyzing, and neutralizing threats. These pipelines aren’t simply about automation of existing tasks; they represent a fundamental shift where agentic AI proactively hunts for anomalies, predicts potential attacks, and implements countermeasures without human intervention. This culmination of years of development integrates advanced machine learning algorithms with comprehensive threat intelligence, allowing the system to adapt and evolve in response to ever-changing adversarial tactics. The result is a self-defending infrastructure capable of operating continuously, reducing alert fatigue for security teams and dramatically improving overall response times – essentially, a cybersecurity system that learns, adapts, and protects with minimal human oversight.

Modern cybersecurity pipelines are increasingly reliant on agentic AI, systems capable of independent decision-making and action in response to threats. These aren’t simply reactive tools; they proactively hunt for anomalies and orchestrate defenses with minimal human intervention. A key enabler of this capability is the ATLAS framework, which provides a structured methodology for analyzing adversarial tactics, techniques, and procedures. By mapping observed attacks onto the ATLAS matrix, the AI can not only identify current threats but also anticipate future ones based on established patterns. This allows for a more nuanced and effective response than traditional signature-based detection, enabling the system to counter sophisticated attacks and zero-day exploits with greater precision and speed. The combination of agentic AI and frameworks like ATLAS represents a significant leap toward truly autonomous threat response.

The true potential of autonomous cybersecurity hinges not solely on advanced AI, but on its seamless integration with established security operations. Comprehensive threat intelligence feeds are critical; these pipelines require constantly updated data on emerging threats, attacker tactics, and vulnerability landscapes to proactively defend against evolving attacks. However, this intelligence is most effective when channeled through existing Security Information and Event Management (SIEM) platforms and Endpoint Detection and Response (EDR) tools. By connecting autonomous systems to these foundational technologies, security teams can extend their capabilities, automate incident response workflows, and ultimately strengthen their overall security posture – transforming raw data into actionable insights and coordinated defenses.

Beyond Benchmarks: Validating and Refining Agentic AI

The escalating complexity of artificial intelligence demands robust evaluation frameworks, and AgentBench and CyBench are emerging as crucial tools in this endeavor. These benchmarks move beyond traditional metrics by assessing an agent’s capacity for autonomous reasoning across a spectrum of challenges – from collaborative tasks and information seeking to complex problem-solving. AgentBench focuses on general agent capabilities, while CyBench specifically targets cybersecurity scenarios, simulating realistic threat landscapes to gauge an agent’s defensive and reactive abilities. Through standardized tests and diverse scenarios, these platforms provide a quantifiable measure of an agent’s performance, facilitating iterative development and ensuring that these systems can reliably navigate and succeed in complex, real-world applications.

Rigorous evaluation of agentic AI necessitates testing beyond synthetic datasets, and current efforts increasingly leverage frameworks designed to mirror real-world adversarial conditions. The MITRE ATT&CK framework, originally developed for cybersecurity, provides a comprehensive knowledge base of adversary tactics and techniques, allowing researchers to simulate sophisticated attacks against autonomous agents. By pitting agents against these modeled threats – spanning reconnaissance, resource development, initial access, execution, persistence, and privilege escalation – developers can assess vulnerabilities and measure resilience. This approach moves beyond simply measuring task completion to evaluating how an agent behaves under pressure, adapts to changing circumstances, and defends against malicious interference, ultimately bolstering confidence in the safety and reliability of these increasingly complex systems.

Current research endeavors are heavily invested in bolstering the proactive defenses of agentic AI systems, moving beyond performance on established benchmarks to prioritize adaptability and resilience. This involves developing algorithms capable of not only recognizing known attack vectors – as tested through frameworks like MITRE ATT&CK – but also generalizing learned strategies to effectively counter novel threats. Future iterations aim to equip these agents with the capacity for continuous learning, allowing them to dynamically adjust to evolving adversarial landscapes and maintain operational effectiveness even when confronted with previously unseen challenges. The ultimate goal is to create AI systems that demonstrate robust, self-improving security postures, minimizing reliance on human intervention and maximizing their capacity to operate autonomously in increasingly complex and hostile environments.

The progression detailed within this study-from isolated large language model reasoning to complex, multi-agent systems-echoes a fundamental principle of structured behavior. Each generation builds upon the last, demonstrating how a seemingly minor architectural decision-the introduction of a new dependency-can dramatically alter the overall system’s capabilities and inherent risks. As Carl Friedrich Gauss observed, “If other objects are mixed in with the observations, it is necessary to eliminate their effect, lest the errors become very great.” This holds true for agentic AI; each added agent or autonomous function introduces potential vulnerabilities and complexities that must be meticulously addressed to prevent cascading failures within the cybersecurity pipeline. The increasing sophistication, while promising, demands a rigorous understanding of these feedback loops.

What Lies Ahead?

The taxonomy presented here – tracing agentic AI in cybersecurity through five generations – isn’t a claim of completion, but a mapping of escalating complexity. Each iteration, from solitary LLM reasoners to the envisioned autonomous pipelines, amplifies both capability and opacity. The crucial question isn’t simply ‘can it detect threats?’, but ‘can the system articulate why, and is that rationale scalable beyond the initial training data?’ Current architectures often resemble elaborate Rube Goldberg machines – impressive in their operation, yet fragile and difficult to truly understand.

The pursuit of ‘autonomy’ risks becoming a distraction. True robustness won’t emerge from increasingly intricate automation, but from elegant, foundational principles. A system built on a shaky understanding of adversarial behavior, however sophisticated, will inevitably exhibit brittle failure modes. The focus must shift from simply doing cybersecurity, to modeling the underlying structure of threats and defenses.

Ultimately, the limitations aren’t computational, but conceptual. Scalable intelligence requires clarity, not complexity. The ecosystem of agentic systems will only thrive if each component – from threat intelligence feeds to response mechanisms – is rigorously vetted for its contribution to the overall systemic health. The next generation will not be defined by faster processing, but by a deeper, more holistic understanding of the problem itself.

Original article: https://arxiv.org/pdf/2512.06659.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/