Editor’s Note: This is Part 1 of our comprehensive 2-part series exploring the impact of Agentic AI in cybersecurity. In this first post, we’ll examine what agentic AI is, how it’s being deployed across cybersecurity operations, and the transformative benefits organisations are already experiencing. Part 2 will dive deep into the security risks, ethical challenges, and practical implementation strategies that leaders must consider.
Agentic AI is revolutionising cybersecurity by enabling autonomous systems that can independently detect, respond to, and mitigate threats. This evolution offers significant benefits but also introduces new challenges and risks that organisations must carefully consider. In this first part of our series, we’ll explore the foundational concepts, real-world applications, and compelling advantages that are driving rapid adoption of these autonomous security systems.
What is Agentic AI?
Agentic AI refers to artificial intelligence systems designed not just to generate outputs, but to also operate autonomously across multiple steps of a decision-making process. Unlike generative AI, which excels at creating content, code, or responses based on prompts, agentic AI systems perceive their environment, reason using models like generative AI, act independently based on that reasoning, and learn from the outcomes to improve over time.
This Perceive–Reason–Act–Learn loop enables agentic systems to go beyond static rules or reactive logic. Agentic AI in Cybersecurity means autonomously monitored network traffic, evaluated anomalies and the deployment of countermeasures. It will help organisations define it’s strategies based on results, without needing a human in the loop for each decision.
Agentic AI is Changing Cyber Offense and Cybercrime
While agentic AI enhances defensive capabilities, it also equips cybercriminals with advanced tools. Malicious actors are leveraging AI to automate and scale attacks, making them more sophisticated and harder to detect. For instance, AI can be used to craft convincing phishing emails, automate vulnerability scanning, and even adapt attack strategies in real-time based on the target’s defences. This dual-use nature of agentic AI necessitates robust security measures to prevent its misuse.
Key Applications of Agentic AI in Cybersecurity
Agentic AI is being rapidly adopted across the cybersecurity stack, introducing a new era of proactive, intelligent defence systems that operate with minimal human input. These AI agents aren’t just enhancing workflows, they’re also redefining how cyber threats are identified, triaged, and neutralised.
Threat Detection and Response
Traditional threat detection relies heavily on static signatures and rule-based systems. Agentic AI, by contrast, applies behavioural analysis and machine learning to detect novel threats that might otherwise evade legacy systems. These agents continuously monitor network traffic and user behaviour, learning what constitutes “normal” and flagging deviations in real time.
For example, Microsoft’s Security Copilot uses advanced language models, the same kind of AI behind tools like ChatGPT—combined with real-time security data to help analysts during a cyberattack. It can generate threat summaries, highlight suspicious activity, and recommend next steps, acting like a tireless assistant that’s on call 24/7.
In essence, agentic AI slashes dwell time… the critical period between intrusion and detection, allowing teams to act before damage compounds.
Security Operations Center (SOC) Automation
SOC teams are often overwhelmed by alert fatigue, drowning in false positives and repetitive triage tasks. Research found that 63% of cyber teams spend 4 or more hours per week dealing with false positives, in fact, 33% of companies have been late responding to a cyberattack because they were dealing with a false positive. Agentic AI can be deployed to triage alerts, escalate only those that require human input, and even draft incident reports automatically.
Companies like IBM are already deploying AI-powered security orchestration tools that perform Level 1 and Level 2 SOC functions autonomously. This allows human analysts to spend their time solving complex problems instead of checking a thousand blinking red lights.
AI agents also assist in log correlation across disparate tools and systems, providing a unified view of an organization’s security posture and enabling faster threat hunting.
Vulnerability Management
Agentic AI revolutionises how organisations handle vulnerabilities, from discovery to patching. Unlike legacy systems that simply generate Common Vulnerabilities and Exposures (CVE) lists, agentic systems prioritise vulnerabilities based on contextual risk factors like exploitability, asset criticality, and exposure level.
Tools such as Tenable and Qualys are embedding AI-driven prioritisation engines to automatically assign remediation timelines and recommend patch schedules based on the business impact of each flaw.
In some cases, agentic systems can even deploy patches without human intervention, particularly in containerised or ephemeral environments where automation is safe and reversible. This closes gaps faster and reduces reliance on manual intervention, a known bottleneck in vulnerability workflows.
Incident Response
When a breach occurs, speed is critical. Agentic AI can take point on initial triage, containment, and recovery. For instance:
- Isolate affected devices from the network.
- Capture and preserve forensic data.
- Launch rollback procedures for compromised configurations.
- Communicate pre-drafted incident notifications to stakeholders.
Moreover, AI-generated timelines and summaries reduce the cognitive load on incident responders and help them make strategic decisions faster.
Benefits and Opportunities for Security Teams
The integration of agentic AI in cybersecurity isn’t just a technological upgrade, it’s a structural transformation of how security operations function. For resource-strapped teams and overworked analysts, these systems offer not just efficiency but strategic leverage.
Enhanced Efficiency
Agentic AI in cybersecurity significantly enhances efficiency within Security Operations Centers (SOCs) by automating repetitive, low-complexity tasks such as log parsing, alert triage, and report generation. This automation alleviates the burden on human analysts, allowing them to concentrate on strategic activities like threat hunting, architecture design, and red teaming.
For instance, Google’s Gemini in Security Operations has demonstrated the capability to automate tasks that previously required significant manual effort. According to Hector Peña, Senior Information Security Director at Apex Fintech Solutions, tasks such as writing regular expressions, which could take analysts anywhere from 30 minutes to an hour, can now be completed within seconds using Gemini.
These advancements not only streamline operations but also contribute to faster incident response times and improved overall security posture.
Improved Threat Detection
Unlike rule-based systems that rely on known signatures, agentic AI uses pattern recognition and anomaly detection to uncover hidden threats, even zero-day exploits or lateral movements that evade traditional defences. By continuously learning from evolving attack tactics, these systems adapt faster than conventional tools and flag risks that would otherwise blend into background noise.
Another page from Google’s playbook, their Chronicle Security analytics platform uses AI to scan petabytes of telemetry data to detect and contextualize advanced persistent threats (APTs) that human teams may overlook.
Faster Response Times
Speed is a critical differentiator in cyber defence. With agentic AI, detection and containment actions can occur in seconds, rather than hours or days. AI agents can instantly isolate compromised endpoints, initiate traffic filtering, or revoke credentials, all without waiting for human input. This translates into a tangible reduction in “dwell time,” the window attackers exploit to escalate privileges or exfiltrate data.
Scalability
Security needs grow exponentially with an organisation’s scale, including new devices, users, cloud services, and third-party integrations. Agentic AI provides horizontal scalability without requiring corresponding increases in headcount. Whether a business has 100 or 100,000 endpoints, agentic systems can ingest and analyse data at machine scale.
This is especially valuable for mid-market and fast-growing companies that can’t yet afford round-the-clock human Security Operations Center (SOC) coverage but face enterprise-level threats.
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Coming Up in Part 2
While agentic AI delivers impressive capabilities and benefits, implementing these autonomous systems introduces significant security risks and ethical challenges that organisations cannot ignore. In Part 2 of this series, “The Challenges of Agentic AI: Security Risks, Ethics, and Implementation Strategy,” we’ll explore:
- Critical security vulnerabilities – From adversarial attacks to data poisoning, learn how autonomous AI systems can be compromised and weaponised.
- Ethical dilemmas and accountability – Who’s responsible when AI makes the wrong security decision? We’ll examine transparency, bias, and regulatory compliance challenges
- Strategic implementation guidance – Practical frameworks for assessing readiness, building infrastructure, and deploying agentic AI safely within your organisation.
- The future landscape – How agentic AI will reshape cybersecurity roles and enable new forms of global threat collaboration.
The promise of agentic AI in cybersecurity is real, but success requires understanding both its transformative potential and its inherent risks. Join us in Part 2 to explore the critical considerations that will determine whether your agentic AI deployment becomes a competitive advantage or a costly liability.
Want to learn more about how AI can level up your cybersecurity? Book a consultation to speak to one of our Cybersecurity Consultants today: Contact Us – medishield.tech