Cybersecurity has always been a race against time. Every second counts between detection and containment, and in today’s high-velocity threat landscape, even the most mature Security Operations Centers (SOCs) struggle to keep up.The rise of Agentic AI, the autonomous systems capable of acting independently within defined boundariesis reshaping how Security Operations Centers (SOCs) defend digital enterprises. We are witnessing a shift from human-dependent, reactive operations to AI-driven, adaptive ecosystems where machines don’t just assist analysts but make containment and remediation decisions on their own.
Director – Partner Engineering
Infosys
From Copilots to Agents: The Next Frontier of Cyber Defense
AI copilots like Palo Alto Cortex XSIAM Copilot, Microsoft Security Copilot and Google Sec LMhave given security analysts valuable support for triaging alerts, creating reports, and summarizing incidents, greatly speeding up routine workflows while still requiring final analyst approval before any action was taken. Now, the field is advancing to a new phase: the platform brings in agentic AI, the autonomous software agents that can identify threats, execute containment strategies, and continuously learn from human feedback, all in real time. Instead of waiting for instructions, these AI agents now sense, decide, and act on their own within policy and risk guidelines. In short, copilots guide your decisions, but the agents will help you take the necessary action.
The Reactive SOC Problem
Traditional SOCs remain constrained by manual investigation and alert fatigue. IBM’s 2024 X-Force report notes that the average organization faces over 11,000 alerts per day, while analysts resolve only a small fraction. Extended dwell time means extended risk.Even with Copilots, analysts spend most of their day validating alerts and cross-checking data. The result is burnout, missed detections, and ballooning response times.
Tools like Palo Alto Cortex XSIAM addresses this bottleneck directly. It uses machine-learning correlation engines to reduce alert noise by up to 80 percent, automatically grouping related signals into a single incident. Instead of chasing thousands of alerts, analysts focus on what matters which is validated, contextualized threats.
Agentic AI in Cyber Defense
Agentic AI systems are built on three pillars: autonomy, adaptability, and accountability.
- Autonomy: The AI executes tasks such as isolating endpoints, resetting credentials, or enriching threat intelligence feeds when confidence scores are high.
- Adaptability: It learns from analyst feedback wherein every accepted or overridden suggestion becomes a new training data point.
- Accountability: All actions are logged, auditable, and reversible, ensuring trust and compliance.
A typical agentic SOC workflow looks like this:
- Telemetry flows into an AI data lake (for example, Palo Alto Cortex XSIAM).
- The agent clusters anomalies, correlates across domains, and identifies incidents.
- Based on confidence thresholds, it autonomously executes containment such as blocking malicious IPs or quarantining endpoints.
- The analyst reviews, adjusts, and reinforces or corrects the AI’s decision.
Over time, this feedback loop creates a self-improving defense fabric, what Palo Alto calls autonomous SOC intelligence.
Human-in-the-Loop vs. Human-on-the-Loop
The distinction between these models is crucial:
- Human-in-the-loop SOCs rely on analysts for validation before every action. The AI assists, but humans decide.
- Human-on-the-loop SOCs trust the AI to act autonomously within boundaries while humans supervise, audit, and fine-tune its behavior.
It’s like shifting from a cockpit where the pilot flies with AI assistance to an autopilot system where the pilot oversees and intervenes only when needed. This evolution does not remove humans; it elevates themfrom operators to strategists, focusing on proactive threat hunting and continuous improvement instead of endless alert fatigue.
Real-World Momentum: XSIAM, Copilot, and Sec-LM
Industry leaders are already exploring this model:
- Palo Alto Networks’ Cortex XSIAM is pioneering autonomous SOC operations through AI agents that correlate multi-domain telemetry, reduce noise, and trigger policy-based actions without human initiation.
- Microsoft Security Copilot is evolving beyond summarization into guided automation, integrating Copilot with Defender XDR and Sentinel workflows for automated containment and recovery.
- Google’s Sec-LM applies natural-language reasoning and contextual containment recommendations across the Chronicle and Mandiant ecosystems.
These examples highlight a convergence of AI and cybersecurity platforms, one where machine autonomy and human judgment coexist within accountable frameworks.
The Governance Question: Can We Trust AI Agents?
The biggest challenge for agentic SOCs is not technology rather it’s trust.Security leaders worry about over-automation, false positives, and compliance violations. A misfired action could disrupt production or violate regulatory controls.
That’s why governance architecture is essential. Modern agentic systems include:
- Policy-based guardrails: Define which actions AI can take autonomously versus which require human review.
- Confidence scoring: Each action carries a verifiable probability score for tuning risk tolerance.
- Explainability reports: Logs show why each decision was made and what data supported it.
- Reversibility: Every action can be rolled back if misapplied.
This ensures AI remains accountable, transparent, and controllable, the principles echoed in the NIST AI Risk Management Framework and the EU AI Act.
Why It Matters: Scale, Speed, and Sanity
The promise of agentic AI isn’t only automation rather it’s augmentation at scale.Enterprises cannot grow their SOCs linearly with threat volume. With a global shortage of over 4 million cybersecurity professionals (ISC²), agentic AI delivers the multiplier effect SOCs need.
By offloading repetitive triage and low-risk containment to machines, human analysts regain time for proactive threat hunting and strategy. Early adopters of AI-augmented SOCs report 50–70% reductions in MTTR and improved analyst satisfactionnot because AI replaced them, but because it removed the noise that once overwhelmed them.
The Road Ahead: From Reactive to Resilient
Agentic AI marks the next major inflection point in Cybersecurity, moving from detection-driven SOCs to decision-drivenecosystems.As platforms mature, expect collaborative autonomy: multiple AI agents specializing in domains like identity, cloud, and network defense, all working in concert under unified policy control.
The SOC of the future will not depend on rows of analysts staring at dashboards. It will be orchestrated by a handful of experts overseeing a digital defense swarm that are intelligent, explainable, and always learning.
Cyber defense is no longer about speed alone. It’s about confidence and control at machine scale. And agentic AI is the bridge that makes it possible. In this new paradigm, humans don’t disappear from the SOC. They lead it and are amplified by intelligent agents that never sleep, never forget, and never stop defending.
Summary:
Agentic AI is redefining SOC operations, enabling autonomous containment, adaptive learning, and human-supervised automation. With platforms like Palo Alto Cortex XSIAM and others leading the charge, the future SOC will be a blend of machine autonomy and human intelligence i.e. faster, smarter, and built for resilience
–Authored by Prassanna Rajgopal, Director – Partner Engineering at Infosys
