Author: Denis Avetisyan
As artificial intelligence evolves beyond passive tools, a new generation of autonomous AI agents is emerging to reshape the landscape of cybersecurity, offering both powerful defenses and novel attack surfaces.
This review surveys the opportunities and challenges of agentic AI in cybersecurity, outlining potential use cases and highlighting the need for updated risk mitigation strategies.
While artificial intelligence has historically focused on narrow, task-specific applications, the emergence of agentic systems-capable of autonomous reasoning, planning, and action-presents a paradigm shift with profound implications for cybersecurity. This survey, ‘A Survey of Agentic AI and Cybersecurity: Challenges, Opportunities and Use-case Prototypes’, examines this duality, revealing how agentic capabilities simultaneously bolster defenses through automated threat response and amplify offensive potential via accelerated attacks. Our analysis demonstrates that existing security frameworks are ill-equipped to address the unique risks posed by persistent, autonomous agents, necessitating novel approaches to governance, assurance, and evaluation. As agentic AI matures, can we proactively establish robust safeguards to harness its benefits while mitigating the escalating threats it introduces?
The Inevitable Overload: Why We Needed Smarter Defenses
Conventional cybersecurity strategies are increasingly challenged by the sheer speed and sophistication of contemporary threats. Historically, defenses have operated on a model of detection and response – identifying malicious activity after it has begun, and then implementing countermeasures. However, the exponential growth in the volume of attacks, coupled with their increasing complexity – often employing techniques like polymorphism and zero-day exploits – overwhelms these reactive systems. Security teams are forced to address a constant barrage of alerts, frequently lacking the time or resources to effectively prioritize and neutralize all threats. This creates a critical vulnerability, as even a single successful breach can have devastating consequences for individuals and organizations alike, highlighting the urgent need for a fundamentally different approach to digital defense.
The evolving landscape of cybersecurity is poised for a transformative shift with the emergence of agentic AI systems, fueled by advancements in Generative AI. These aren’t simply faster versions of existing tools; they represent a move from reactive threat response to proactive defense. By leveraging the reasoning and creative capabilities of Generative AI, these systems can independently analyze network behavior, predict potential vulnerabilities, and autonomously implement countermeasures – all without direct human intervention. This capability promises to address the limitations of traditional, rule-based security which struggles to keep pace with increasingly sophisticated and rapidly evolving threats. The potential impact extends beyond simple automation, offering the possibility of truly intelligent security that learns, adapts, and anticipates attacks before they can inflict damage.
The advancement of autonomous intelligence in cybersecurity centers on a departure from traditional, static rule-based systems. These conventional defenses, while valuable, struggle to adapt to rapidly evolving threats and often require human intervention to define and implement countermeasures. Current innovation focuses on agentic systems – powered by Generative AI – capable of independent reasoning. These systems don’t simply react to known attack signatures; they analyze situations, formulate plans, and make decisions autonomously. This capability extends beyond pattern recognition to encompass threat hunting, vulnerability assessment, and even proactive mitigation strategies. By simulating cognitive processes, these intelligent agents can anticipate attacks, dynamically adjust defenses, and operate at a scale and speed impossible for human analysts or pre-programmed systems, ultimately promising a more resilient and adaptable cybersecurity posture.
Automating the Grind: Letting the Machines Handle the Noise
The integration of agentic AI into Security Operations Centers (SOCs) is focused on automating the initial phases of incident handling, specifically alert triage and response. Current deployments utilize Large Language Model (LLM)-assisted agents to analyze security alerts and reduce the workload on human analysts. Performance metrics demonstrate a reduction in Mean Time To Triage (MTTR) from 7.5 minutes to 6.0 minutes with the implementation of these agents, indicating a quantifiable improvement in operational efficiency. This automation aims to decrease analyst fatigue and accelerate the overall incident response lifecycle by prioritizing and categorizing alerts before human intervention.
Intelligent agents are substantially improving threat intelligence capabilities by autonomously gathering and analyzing data from diverse sources, including open-source intelligence (OSINT) feeds, dark web forums, and vulnerability databases. This proactive approach allows for the early identification of emerging threats – previously undetectable until actively exploited – and the automated characterization of their potential impact. Agents perform automated analysis of threat actors, tactics, techniques, and procedures (TTPs), and correlate this information with internal network data to assess risk and prioritize mitigation efforts. The result is a shift from reactive incident response to a more preventative security posture, enabling organizations to anticipate and neutralize threats before they materialize.
Autonomous agents are being deployed to enhance both offensive and defensive cybersecurity strategies. In Red Teaming exercises, these agents simulate attacks to proactively identify system vulnerabilities before exploitation. Concurrently, Blue Teaming benefits from automated defense mechanisms facilitated by these agents. Evaluations of LLM-assisted Intrusion Detection Systems (IDS) demonstrate a significant improvement in Recall, achieving a score of 1.0 compared to the 0.75 Recall rate of baseline IDS implementations. This indicates a substantial reduction in false negatives and an increased ability to accurately identify malicious activity.
Giving Agents a Memory: Beyond Pattern Matching
Agentic memory represents a significant advancement in autonomous system capabilities, allowing for persistent data storage and retrieval beyond the immediate context window of a Large Language Model (LLM). This capability facilitates long-term reasoning by enabling agents to learn from past observations, identify patterns in previously encountered threats, and apply that knowledge to future scenarios. Unlike stateless systems, agents equipped with memory can adapt their behavior over time, improving performance through experience and reducing reliance on constant retraining. The implementation of agentic memory involves techniques like vector databases and knowledge graphs to efficiently store, index, and retrieve relevant information, thereby enabling more informed decision-making and proactive threat response.
Reinforcement Learning (RL) enables the training of cybersecurity agents through a trial-and-error process within a simulated or real-world environment. The agent learns to maximize a cumulative reward signal by selecting actions that lead to desired outcomes, such as successfully identifying and mitigating threats. This approach is particularly effective in dynamic environments where pre-programmed rules are insufficient due to the constantly evolving nature of cyberattacks. Unlike supervised learning, RL does not require labeled datasets of attack scenarios; instead, the agent actively explores the environment and learns from the consequences of its actions. Algorithms such as Q-learning and Deep Q-Networks (DQN) are commonly employed to approximate the optimal policy for making decisions, allowing agents to adapt to novel threats and improve their performance over time without explicit reprogramming.
Multi-Agent Systems (MAS) extend cybersecurity capabilities by distributing tasks and coordinating responses across multiple agents, enabling the handling of threats that are too complex for individual agents to address effectively. This approach allows for parallel processing of data, improved coverage of the attack surface, and enhanced resilience to failures. Recent implementations, such as an LLM-assisted Intrusion Detection System (IDS), have demonstrated significant performance gains; specifically, this system achieved a perfect F1-Score of 1.0, indicating both high precision and recall in threat identification and minimal false positives or negatives.
The Coming Storm: Quantum Computing and the Illusion of Security
The advent of quantum computing presents a fundamental challenge to modern cybersecurity infrastructure, as currently employed cryptographic algorithms – such as RSA and ECC – are vulnerable to attacks from sufficiently powerful quantum computers utilizing algorithms like Shor’s algorithm. This isn’t a distant threat; the potential for “store now, decrypt later” attacks motivates immediate action. Consequently, the field of Post-Quantum Cryptography (PQC) is rapidly developing and standardizing new cryptographic algorithms believed to be resistant to both classical and quantum attacks. These algorithms, often based on mathematical problems like lattice-based cryptography, code-based cryptography, and multivariate cryptography, represent a crucial shift in securing digital information and maintaining trust in online systems. Transitioning to PQC is a complex undertaking, requiring updates to software, hardware, and security protocols across all sectors, but it is essential to proactively safeguard data against the looming threat of quantum-enabled decryption.
As computational landscapes shift with the advent of quantum computing, agentic artificial intelligence systems face an evolving threat profile demanding proactive adaptation. Current cryptographic standards, foundational to secure communication and data protection, are vulnerable to quantum attacks, necessitating a transition to post-quantum cryptography. Agentic AI, designed to operate autonomously and make independent decisions, must therefore integrate quantum-resistant algorithms not merely as an upgrade, but as a core component of its operational framework. Furthermore, these intelligent agents require the development of novel defense strategies; anticipating potential quantum-enabled exploits and dynamically adjusting security protocols will be critical. This demands ongoing research into adaptive security mechanisms, allowing agentic AI to maintain robust protection against increasingly sophisticated threats in a post-quantum world.
The secure deployment of agentic AI systems hinges on a proactive approach to inherent vulnerabilities, particularly those stemming from prompt injection attacks. These attacks exploit the reliance of large language models on natural language input, allowing malicious actors to manipulate agent behavior through carefully crafted prompts. Beyond prompt injection, agentic security risks encompass a broader range of potential failures, including data breaches, unauthorized actions, and unintended consequences resulting from flawed decision-making processes. Mitigating these risks requires a multi-faceted strategy encompassing robust input validation, anomaly detection, continuous monitoring, and the implementation of safeguards to limit agent autonomy in critical operations. Prioritizing these security measures isn’t merely a technical necessity; it’s fundamental to fostering public trust and ensuring the responsible integration of agentic AI into society.
The effective and responsible deployment of agentic AI systems hinges on the establishment of robust governance frameworks, acknowledging their increasing autonomy and potential impact. Recent studies demonstrate the viability of leveraging large language models within intrusion detection systems (IDS) to not only identify threats but also to provide understandable explanations of those threats; evaluations indicate these LLM-generated explanations achieve a high degree of usefulness, averaging a rubric score of 0.83. This ability to articulate reasoning is crucial for building trust and enabling effective human oversight, allowing security professionals to validate alerts and refine system responses. Consequently, a proactive approach to agentic governance, incorporating explainable AI and clear accountability structures, is no longer optional but essential for harnessing the benefits of these powerful technologies while mitigating inherent risks.
The pursuit of autonomous cybersecurity, as outlined in this survey of agentic AI, feels predictably optimistic. It proposes elegant solutions – autonomous threat response, enhanced intelligence – but fundamentally ignores the inevitable march toward technical debt. The system will be compromised, not because of a flaw in the initial design, but because production environments are inherently chaotic. As Claude Shannon observed, “The most important innovation we can make is to figure out how to use information more effectively.” This sentiment rings hollow when applied to agentic systems; more information simply creates more attack surfaces and more complex failure modes. The promise of simplification is a fiction; each layer of abstraction introduces new vulnerabilities, turning a theoretical advantage into a practical headache. This isn’t pessimism, it’s merely acknowledging the entropy inherent in complex systems.
What Comes Next?
The exploration of agentic AI in cybersecurity inevitably circles back to the fundamental problem of trust. These systems, by design, operate with a degree of autonomy that renders traditional security models – predicated on human oversight and intervention – increasingly inadequate. The prototypes detailed within offer glimpses of potential, but each autonomous action is, at its core, a calculated risk. Tests are a form of faith, not certainty; a script that performs beautifully in a sandbox will, without fail, find a novel way to disrupt production.
Future research will likely concentrate on the ‘containment’ problem – not preventing failures, but minimizing their blast radius. The focus shifts from building intelligent agents to building resilient systems around them. Expect a surge in work regarding verifiable AI, formal methods for agent behavior, and the development of ‘kill switches’ that are, ironically, as complex and fallible as the agents they are meant to control. The pursuit of ‘perfect’ autonomy is a distraction; useful systems will be those that gracefully degrade, not those that attempt the impossible.
Ultimately, the field will be defined not by elegant architectures or benchmark scores, but by the mundane reality of maintaining these systems through endless Monday morning incidents. The question isn’t whether agentic AI will be a security boon, but whether the cost of managing its inevitable failures will be less than the threats it purports to address. The problem, as always, isn’t the technology, but the operational overhead.
Original article: https://arxiv.org/pdf/2601.05293.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2026-01-12 08:53