What is Agentic Security? Everything You Should Know

Security operations centers (SOCs) face an overwhelming volume of security alerts each day. According to a 2023 survey report from Cyberreason, more than a third of respondents said they received between 10,000 - 15,000 alerts per day, leading to rampant alert fatigue​ and analyst burnout. Analysts must sift through this constant barrage of alerts, manually piecing together evidence and correlating data, yet many alerts that demand investigation ultimately prove to be false positives or benign events​.

The result is a tremendous drain on time and focus. It’s no surprise that burnout is epidemic among SOC analysts. A recent study found 71% of SOC analysts experience burnout, with many considering leaving their jobs within a year​.

Hiring more analysts might seem like the obvious fix, but it’s far from sustainable. The combination of a persistent cybersecurity talent shortage and budget constraints makes it increasingly difficult to scale teams. Recruiting, onboarding, and retaining skilled SOC analysts is expensive and time-consuming. Yet that’s the only feasible (and expensive) path for teams that continue to rely on human-driven investigations.

With teams stretched thin and struggling to triage every alert, critical threats can be missed or addressed too slowly.

To combat these challenges, organizations are turning to a new paradigm: Agentic Security powered by AI Agents. But what exactly is Agentic Security? 

What is Agentic Security and how does it relate to Agentic AI?

Agentic AI describes autonomous AI agents capable of independently pursuing specified objectives. Agentic Security is a specialized application of Agentic AI, using autonomous agents specifically for proactive security tasks, such as alert triage, investigation, and response.

Unlike simpler automation tools or "AI security copilots" that primarily support human analysts when prompted, Agentic Security autonomously investigates alerts and initiates appropriate responses without needing explicit direction at each step. 

By automating the heavy lifting involved in routine investigations, Agentic Security reduces alert fatigue, lessens analyst workloads, and accelerates threat response. 

While "Agentic Security" and "Agentic AI" represent distinct concepts—the former being a security-specific use case of the latter—we will use these terms interchangeably throughout this blog for clarity and ease of understanding. 

In the sections that follow, we explore what makes Agentic Security unique, differentiate it clearly from security copilots, and highlight how it transforms alert triage and investigation to effectively address real-world SOC pain points.

We will also guide you through Agentic Security best practices in order to ensure successful implementation. 

Agentic Security vs. Security Copilots

While Agentic Security operates proactively, traditional Security Copilots act as reactive assistants or chatbots, requiring explicit analyst input for each step of an investigation. Security Copilots rely heavily on continuous human interaction, limiting their ability to deliver substantial efficiency gains.

By contrast, Agentic Security autonomously handles routine investigative processes without waiting for analyst prompts, drastically reducing investigation times and enabling analysts to focus solely on complex incidents requiring human judgment.

The key is understanding where full autonomy versus guided assistance fits best in your security operations.

How Agentic Security automates alert triage and investigation

Agentic Security shines in its ability to automate the alert handling process from start to finish. Also known as Agentic AI SOC Analyst, it essentially performs the duties of a tireless Tier-1/Tier-2 SOC analyst at machine speed. Some of the key capabilities of an Agentic Security system in alert handling include:

• Automated Tier-1/Tier-2 triage: The AI can perform the initial alert analysis and enrichment that a junior analyst would do, collecting all relevant context automatically​.
  
  It emulates the investigative process of human analysts by querying logs, checking asset and identity information, and gathering threat intelligence – but it does so in seconds, ensuring no analyst time is wasted on data gathering and noise.
  
  
• Context enrichment and correlation: Instead of an analyst manually pivoting between different security tools, the AI pulls in data from across the environment and correlates it. For a given alert, it might gather endpoint telemetry, network traffic logs, user login history, and external threat intel, then connect the dots. This comprehensive context helps determine if an alert represents a real incident or a false alarm, and it builds a full narrative around the event for the analyst to review.
  
  
• Noise filtering (false positive reduction): Agentic Security can quickly recognize benign patterns and dismiss false positives with minimal human input. SOC teams don’t need better alerts, they need faster investigations. By autonomously applying deeper analysis to each alert, the AI rapidly identifies benign alerts and resolves them without burdening analysts. It effectively filters out the “noise” so that analysts only see alerts that truly warrant attention.
  
  
• Immediate response actions: For clear-cut threats, the AI is capable of taking predefined response steps on its own. For instance, it might isolate an affected host from the network, quarantine a malicious file, or disable a compromised user account as soon as it confirms malicious activity​.
  
  These actions occur in real time, potentially containing threats within moments of detection. By handling low-level remediation autonomously, Agentic Security systems drastically reduces response time and allows the SOC to contain incidents before they spread widely.

• Continuous learning and adaptation: Agentic Security systems improve over time. They learn from each investigation and from any feedback analysts provide on their conclusions. For example, if an analyst marks an AI-determined false positive as truly benign, the system incorporates that feedback. 
  
  Like an experienced analyst gaining intuition, the AI gets better the longer it’s in use – adapting to an organization’s unique environment, identifying recurring patterns, and making more precise decisions as it processes more alerts.

• 24/7 operation: Unlike human staff, an AI agent never needs sleep. It provides around-the-clock coverage, investigating alerts as they come in. Continuous AI-powered alert monitoring ensures that critical alerts are not waiting hours in a queue until a morning shift.

All these capabilities allow Agentic Security systems to accelerate the alert-handling process dramatically. Routine triage and investigation that might take a human 30–60 minutes can be accomplished by AI in minutes or seconds. 

The AI essentially serves as a tireless Level-1/Level-2 analyst that handles the bulk of alerts, freeing human team members to focus on the most complex or high-impact incidents that truly require human judgment.

Secondary benefits: reducing alert fatigue, burnout, and turnover

One of the most immediate benefits of Agentic Security in security operations is a sharp reduction in alert fatigue. Alert fatigue occurs when analysts are exposed to a high volume of alerts (many of them low-value) and start to become desensitized or overwhelmed, potentially missing real threats amid the noise. 

By deploying autonomous AI agents for triage and investigations, organizations can dramatically cut down the number of trivial or false alerts that reach human analysts.

Autonomous triage not only cuts noise, but it also ensures consistency – every alert gets at least a preliminary investigation. This approach eliminates the dangerous scenario of alerts being ignored due to overload. Teams often find themselves making the tough decision to ignore low or medium severity alerts. 

Agentic Security systems empower SOCs to finally investigate every alert without adding additional resources.

By reducing tedious workloads, agentic AI also plays a key role in preventing analyst burnout and attrition. Burnout in cybersecurity is a well-documented problem – beyond the earlier statistic of 71% of SOC analysts feeling burnt out, consider that 64% of analysts spend over half their time on tedious manual work like reporting and monitoring, which directly lowers job satisfaction. 

The reduction in context switching and tedious busywork means analysts end their days feeling more empowered. 

Moreover, because Agentic Security provides around-the-clock coverage, SOC teams can avoid forcing analysts into overnight on-call rotations or understaffed late shifts. 

Implementing Agentic Security in the SOC

Adopting Agentic Security in a SOC requires careful planning and integration. To fully realize its benefits, SOC managers and CISOs should approach implementation strategically, keeping both technical and human factors in mind. 

Here are key insights and best practices for implementing and integrating agentic AI into security operations.

1. Start with T1/T2 use-cases and keep humans in the loop

Rather than seeking “full automation” from day one, begin by leveraging agentic AI for Tier 1 triage and investigations, while keeping your analysts in the loop for review.

This human-in-the-loop model ensures that analysts still oversee the process and can double-check the AI’s work on the trickier alerts. It builds trust in the AI while still drastically reducing manual workload.

2. Establish guardrails for AI actions

Define clearly what the Agentic Security system is allowed to do on its own, and where human approval is required. For example, you might permit the AI to auto-close alerts it deems benign or to quarantine a file flagged as malware, but require an analyst’s sign-off to disable a user account or shut down a server. 

Keep a human in the loop for high-impact or irreversible actions​, especially during initial deployment. As confidence in the AI grows, these guardrails can be adjusted, but it’s wise to err on the side of caution early on.

3. Integrate the AI with your security stack

Effective Agentic Security implementation hinges on data access. The AI needs to pull from your SIEM, EDR (endpoint detection and response), identity and access management logs, network telemetry, cloud security logs, etc. Ensure the platform you choose has broad integration capabilities. Many enterprises operate a patchwork of security tools that don’t seamlessly interoperate – this integration complexity can be a hurdle to AI success​.

It may be necessary to use APIs or brokers to connect tools. The goal is to give the AI a comprehensive view of your environment; otherwise, blind spots will remain. Additionally, maintain those integrations over time (updates, new log sources, etc.) so the AI continues to have up-to-date data.

4. Ensure transparency and explainability

To gain analyst buy-in and maintain oversight, the Agentic Security system should function as a “clear glass box,” not a black box. Choose a solution that provides transparent, evidence-backed explanations for its conclusions. Analysts should be able to see why the AI categorized an alert as malicious or benign – for example, which log events or anomaly patterns led to that decision​.

This might be delivered as an investigation report showing the steps the AI took and the findings at each step. Transparency is crucial for trust; if the team understands the AI’s reasoning, they can more easily validate and accept its decisions. 

It also helps for compliance and auditing – being able to explain how an incident was handled by AI is important for accountability. 

5. Train and socialize your team

Introducing agentic AI is not just a technical implementation, but a change management exercise. Engage your SOC analysts early and train them on how to interact with the AI system. 

Make sure they know how to interpret AI-generated investigations, how to provide feedback or corrections, and how the workflow will change. Emphasize that the AI is there to augment their capabilities, not replace them – it handles the grunt work so they can focus on higher-level analysis. 

Analysts should feel empowered by having an AI “teammate.” 

6. Monitor metrics and iterate 

Once the Agentic Security system is in production, closely monitor key SOC metrics to gauge its impact. Track things like: 

• Mean time to investigate or respond (MTTI/MTTR)
• Number of alerts handled autonomously
• Number of false positives analysts must review
• Accuracy rate in identifying true positives and false positives
• Analyst workload hours

These metrics will show where the AI is delivering value and where adjustments might be needed. 

7. Mitigate skill erosion and retain human expertise 

A potential long-term side effect of heavy automation is the erosion of human investigative skills.​

If entry-level analysts no longer get to practice triage because the Agentic Security system does it all, how will they develop into senior analysts? 

SOC leadership should consciously manage this by continuing to involve humans in the process in thoughtful ways. 

This could mean rotating team members into threat hunting and detection engineering projects to keep their skills sharp, or having analysts periodically review closed alerts to ensure they agree with the AI’s decisions (a form of quality control that doubles as training). 

The bottom line: don’t let your team’s analytical muscles atrophy. Use the AI to handle volume, but still challenge your people with interesting problems and encourage them to delve into the complex incidents that the AI flags. 

This maintains a healthy symbiosis between AI efficiency and human expertise.

8. Deploy gradually and expand scope over time

It’s often wise to roll out agentic AI in phases. You might start with the AI in “observe mode” where it generates investigation results but doesn’t take automated actions, allowing the team to validate its output for a period of time. 

Then let it handle a specific class of alerts (for example, endpoint malware alerts) autonomously, while still manually handling others. As confidence grows, expand the AI’s scope to more alert types and enable more automated responses. 

A phased deployment helps build trust and ensures that any kinks are worked out on a smaller scale before wider rollout. It also gives your processes time to adapt. 

Over a few months, you may move from mostly human-driven with AI suggestions, to mostly AI-driven with human oversight. Throughout this journey, continue to provide feedback to the AI and refine the system.

By following these implementation practices, SOC managers can integrate Agentic Security smoothly into their operations and avoid common pitfalls. The end result should be a joint human–AI workflow where the AI handles the heavy lifting and routine decisions, and humans provide direction, oversight, and expertise for the tough cases. 

When done right, an Agentic Security system becomes a trusted virtual team member that significantly amplifies the capability of your security operations.

Conclusion

Agentic AI has emerged as a powerful ally for security operations, particularly in the realms of alert triage and investigation. By leveraging autonomy and machine-speed analysis, it tackles the volume and velocity challenges that overwhelm human analysts, reducing alert fatigue, shortening response times, and easing the burden of repetitive work. 

For SOC managers and CISOs, embracing Agentic Security can yield substantial gains in efficiency and threat response, but it must be done thoughtfully. Success lies in finding the optimal balance between AI autonomy and human expertise. Automation should make security teams more effective, not obsolete​.

By empowering analysts with agentic AI tools, organizations can build a more resilient, scalable security operation – one that keeps pace with the threat landscape while maintaining the insight and judgment that only seasoned professionals can provide.