By Neehar Pathare, MD, CEO & CIO, 63SATS Cybertech
In today’s rapidly evolving cyber threat landscape, traditional approaches to threat detection and response are struggling to keep pace.
Enter Agentic AI—a breakthrough in artificial intelligence (AI) that is redefining the way security operations centers (SOCs) function. Designed for autonomy, context awareness, and real-time decision-making, agentic AI represents a substantial leap beyond rule-based automation or narrow AI tools.
Unlike previous generations of AI, agentic AI systems are capable of perceiving, reasoning, and acting independently. These systems can perform multi-step tasks and make decisions without human intervention, all while adapting continuously to changing environments.
In the context of cybersecurity, this translates to faster detection, smarter triage, and proactive response capabilities—essential in a world plagued by alert fatigue and skill shortages.
From Alerting to Autonomous Action
Imagine a cybersecurity setup where AI doesn’t just flag suspicious activity—it actively investigates and mitigates threats in real time. That’s the promise of agentic AI. It not only assists but also augments human expertise by performing repetitive and complex tasks autonomously. Security professionals are then free to focus on high-impact, strategic issues rather than drowning in a sea of false positives.
On of our partners Securonix is actively building the revolutionary framework for an agentic mesh – a self-orchestrating interconnected system of agents that share memory, context, and intent to solve problems together. In this agentic mesh, agents will register themselves, execute jobs to be done, and coordinate with other agents or humans to get jobs done.
agentic philosophy is grounded in trust and transparency. Agents may act autonomously, but analysts retain full control. Organizations define the rules of engagement—role-based permissions, escalation paths, and playbook override logic—to ensure that human intent remains central to every action. This balance ensures speed without sacrificing oversight. The system adapts but never overrides human authority.
Key Applications in Security Operations
Autonomous Threat Remediation: Unlike traditional SOC tools that depend on human intervention, Agentic AI can independently plan and execute defense actions.
Alert Fatigue Management: SOC teams face high volumes of false-positive alerts. Agentic AI filters, prioritizes, and summarizes these, enabling analysts to focus on real threats. According to Ponemon Institute, 57% of SOC staff suffer burnout due to alert overload—something Agentic AI helps reduce.
Efficiency and Scalability: Agentic AI automates tasks like log reviews, incident correlation, and compliance reporting. This boosts efficiency and scalability without increasing staff, lowering costs while improving performance.
Practical Use Cases in Cybersecurity
- Incident Response:
AI agents respond to threats autonomously—isolating endpoints, adjusting firewall rules, and reducing dwell time and damage.
- Vulnerability Management:
By continuously scanning, prioritizing, and even patching vulnerabilities in sandboxed environments, Agentic AI enables proactive defense.
- SIEM Alert Triage:
It filters noise, correlates events, and prioritizes alerts, ensuring analysts focus only on critical incidents.
- Threat Hunting:
Learning normal patterns, Agentic AI autonomously detects anomalies, traces exfiltration, and uncovers stealthy attacks, enhancing human-led analysis.
Benefits of Agentic AI in Cybersecurity
- Faster Detection and Response: Reduces Mean Time to Detect (MTTD) and Mean Time to Contain (MTTC).
- Improved Accuracy: Minimizes human error and enhances decision quality.
- Scalability: Addresses growing security needs without additional staffing.
- Cost Efficiency: Automates repetitive, time-consuming tasks, easing budget pressures.
Navigating the Risks and Responsibilities
Despite its transformative potential, agentic AI introduces several critical challenges that organizations must address. These include establishing governance and accountability for autonomous decisions, ensuring explainability to build trust, and maintaining compliance with evolving regulations and data privacy laws. Seamless integration with existing infrastructure and rigorous model training and validation are also essential to ensure reliability and avoid unintended consequences.
Who is responsible when an AI system makes a critical security decision? Clear governance models, ethical standards, and ongoing system audits are essential to ensure responsible deployment.
Agentic AI is not a silver bullet—but it is a powerful step toward autonomous, intelligent, and resilient cybersecurity. By augmenting human capabilities, addressing resource shortages, and reducing response times, it holds the key to securing digital infrastructure at scale.
However, organizations must approach implementation with caution, ensuring strong ethical guidelines and regulatory alignment to truly benefit from this game-changing technology.
