Agentic AI: Transforming Modern Security Operations
Agentic artificial intelligence (AI) is reshaping the landscape of IT security, moving swiftly from conceptual demos to real-world deployment in security operations centers (SOCs). Unlike traditional automation, agentic AI utilizes intelligent software agents capable of interpreting signals, correlating logs, enriching alerts, and even initiating containment actions. This shift allows human analysts to focus on strategic, high-level investigations while the agents manage repetitive tasks.
“Agents act as digital tier-one analysts,” says Vinod Goje, an expert in applied AI. These systems triage alerts, contextualize data, and generate reports, streamlining security workflows. According to Jonathan Garini, CEO of fifthelement.ai, agentic AI’s primary value lies in alleviating the “repeatable grind,” enabling teams to scale response capabilities and reduce alert fatigue.
Strengths and Limitations of Agentic AI
Agentic AI shines in the “first 15 minutes” of an incident, as Itay Glick of OPSWAT explains. It efficiently summarizes logs, checks threat intelligence, and suggests action plans. AI agents are also proving helpful in prioritizing vulnerabilities, identifying stale accounts, and clustering alerts to reduce noise. Natural language processing (NLP) tools enhance this by summarizing alerts at scale.
However, challenges remain. Glick cautions that agents may stumble without clean data or clear playbooks. False positives, overfitting, and ambiguity in threat signals continue to hinder performance. Prashant Jagwani of Mphasis notes that the best-trained agents still struggle with complex, multi-layered contexts, underscoring the need for human oversight.
Choosing Between Add-Ons and Standalone Frameworks
Organizations face a pivotal choice between integrating AI agents as add-ons to existing platforms or deploying them within standalone frameworks. Add-ons offer quicker implementation with minimal disruption, especially when built on top of SIEM or SOAR systems. “They provide quick wins,” says Garini, particularly when integrated into established pipelines.
Standalone frameworks, on the other hand, offer greater flexibility and control but demand more resources for orchestration and governance. Amit Weigman of Checkpoint highlights Microsoft’s Security Copilot, Google’s Gemini-powered agents, and CrowdStrike’s solutions as examples of successful bolt-on models. These options allow security teams to adopt agentic AI incrementally while maintaining current operations.
Fergal Glynn from Mindgard emphasizes the tradeoff: “Add-ins are easy to deploy but less dynamic, while standalone systems offer customization at a higher cost.” Jagwani adds that most enterprises start with add-ons and gradually evolve toward standalone systems when ready to centralize across hybrid or multicloud environments.
Governance and Organizational Adaptation
Agentic AI adoption requires adjustments to governance models. Teams must adapt existing change-control policies and risk tolerances to fit AI workflows. Glick explains that instead of replacing frameworks, organizations map them into the agent lifecycle with safeguards like two-person sign-offs and sandbox testing.
Red teams now test agents for vulnerabilities through prompt injections and jailbreak attempts. Jagwani notes that explainability is crucial, especially in regulated sectors. “Audit trails must deconstruct the agent’s decisions into inputs, confidence scores, and escalation logic,” he says.
Trust, Oversight, and Human Collaboration
Despite the promise of autonomy, many organizations hesitate to grant agents full control. Goje warns that agents executing unverified actions can introduce new risks, especially in production environments. Transparency is key to overcoming this reluctance. “AI feels like a black box,” says Weigman. Without clear visibility, trust is difficult to establish.
To address this, experts recommend building accountability into AI workflows. Audit trails, documentation, and explainability mechanisms are essential. Kurdziolek from BigID stresses the need for both “what” and “why” documentation to satisfy regulatory requirements.
Still, human collaboration remains vital. Agents are increasingly seen as collaborative allies, with humans retaining final decision-making authority. Weigman suggests deploying specialized agents with narrow scopes to improve transparency and monitoring.
Economic Considerations and ROI Evaluation
For many security leaders, the decision to adopt agentic AI revolves around ROI. While usage-based pricing models are common, Garini notes that tying costs to analyst hours saved can better demonstrate value. Glynn and Glick caution about hidden expenses like API fees and model maintenance, which can inflate total costs quickly.
Pricing models vary widely—from per-seat and per-alert charges to hybrid models. Chakravarty of Black Duck points out that organizations must consider infrastructure costs for running AI models both on-prem and in the cloud. Jagwani warns that simple pricing metrics may overlook the cost of retraining models or building structured telemetry pipelines.
Kurdziolek believes ROI should be measured by time saved in triage and investigation, as well as improvements in incident detection. The real question, he says, is “Are agents helping security teams operate more efficiently and securely?” The answer to that question will determine whether agentic AI becomes a mainstay in cybersecurity or fades as a passing trend.
This article is inspired by content from Original Source. It has been rephrased for originality. Images are credited to the original source.