Agentic AI In Cybersecurity: SOC Automation Led by AI Agents

Artificial intelligence is no longer just hype, but many organizations still struggle to use AI to solve real-world problems. Agentic AI changes that by offering what generative AI chatbots or image creation tools can’t: autonomous agents that can make complex decisions and take action on their own.

In security operations, agentic AI utilizes specialized agents for SOC automation to analyze data, identify patterns, and execute tasks far more efficiently than manual approaches. By cutting short the time it takes to contain threats, agentic AI prevents attackers from moving laterally within your environment and frees up time for SOC analysts to focus on strategic tasks.

In this blog, we’ll explain what agentic AI is and how to apply agentic AI to the modern SOC for game-changing results.

What Is Agentic AI?

Agentic AI is an advanced type of artificial intelligence that works autonomously, making decisions and taking actions without needing human involvement. It relies on AI agents— specialized entities capable of analyzing data, solving problems, and executing tasks on their own. They are not only reactive but proactive, adapting to new information and collaborating with other agents or systems to efficiently achieve complex objectives.

Key features of agentic AI include:

• Autonomy: Operates without constant human oversight.
• Scalability: Handles increasing workloads with ease.
• Speed: Responds to changes in real time.

By taking over repetitive or high-pressure tasks, agentic AI helps organizations work more effectively, reduce the burden on their teams, and focus on long-term strategy and innovation.

What Is an AI Agent?

AI agents are the building blocks of agentic AI, each of which is designed to perform a specific task. These agents can monitor systems, automate repetitive tasks, and even collaborate to achieve more complex objectives.

How AI Agents Work

Some of the core technologies behind AI agents include:

• Machine Learning: Enables AI systems to learn from past data and improve performance over time.
• Behavioral Analytics: Identifies patterns and anomalies in real time, offering predictive insights.
• Generative AI: Advanced neural networks or large language models create outputs based on patterns and data they’ve learned.

Agentic AI works by combining these technologies to assess risks, prioritize actions, and execute responses autonomously.

What are Multi-Agent AI Systems?

In a multi-AI-agent system, several specialized agents each handle a distinct job under the guidance of an orchestrator agent. Each agent has its responses coordinated and validated by the orchestrator agent.

Multi-AI-agent systems offer several key advantages:

• Distributed Analysis: Breaks down complex problems into manageable parts, analyzing them from multiple angles.
• Coordinated Strategy: Executes actions efficiently under the guidance of the orchestrator agent.
• Improved Accuracy and Reliability: Cross-validation between agents reduces hallucinations and ensures reliable data.
• Enhanced Scalability and Efficiency: Adapts to growing workloads and complex environments with ease, even creating new agents where needed.

Benefits of Agentic AI in Cybersecurity

Modern cybersecurity challenges require tools that can keep pace with increasingly sophisticated threats. Agentic AI is capable of keeping up with workloads that would be overwhelming for a typical SOC, helping to reduce alert fatigue, contain threats faster, and prevent lateral movement within an organization’s environment.

Faster Threat Detection and Response: Agentic AI can analyze vast amounts of data in real time, identifying threats and acting to contain them within minutes. This also reduces the mean time to detect and respond, minimizing potential damage from cyberattacks.

Automation of Routine Tasks: By handling repetitive processes like triaging alerts and managing system updates, agentic AI eliminates Tier 1 and Tier 2 tasks. This frees security teams to focus on high-value, strategic tasks, improving overall security posture.

Proactive Threat Containment: Agentic AI prevents attackers from moving laterally within a network by autonomously isolating threats before they spread. This helps organizations maintain control over their environments and reduce the scope of potential breaches.

Applications of Agentic AI in Cybersecurity

SOC teams require tools that can optimize workflows and enhance key processes. Agentic AI plays a pivotal role in areas such as SOC automation, threat containment, and threat hunting, enabling teams to handle evolving challenges with greater efficiency and precision.

SOC Automation

In security operations centers (SOCs), agentic AI contributes to. SOCs are responsible for monitoring and managing an organization’s security posture, which involves analyzing vast amounts of data to detect and respond to threats. Agentic AI can streamline these processes—for example, it can autonomously collect and synthesize related artifacts to enrich alerts.

This level of automation not only reduces threat containment and response times but also minimizes the risk of human error and alleviates analyst fatigue.

Threat Containment and Remediation Actions

One of the standout benefits of an agentic AI architecture is its ability to rapidly and autonomously initiate containment and remediation actions. When a security threat is identified, speed is critical. Agentic AI can immediately contain the threat, preventing attackers from causing further damage. For example, if a malware infection is detected, the AI system can isolate the affected devices and stop the malware from propagating across the network.

Agentic AI can also execute remediation actions to neutralize the threat. This may involve removing malicious files, patching vulnerabilities, or restoring systems from clean backups.

By handling these tasks autonomously, agentic AI minimizes the effort needed from an analyst, ensures that security incidents are addressed promptly and efficiently, and neutralizes disruption to business operations.

Become an AI-Driven SOC: Frameworks and Best Practices

Every modern security operations team needs to automate if they want to survive, but it can be overwhelming to start this process. Switching to an AI-driven SOC is the best approach, utilizing technologies like agentic AI to automate routine tasks, detect threats faster, and respond more efficiently. By integrating agentic AI, SOC teams can reduce workloads, improve accuracy, and scale their operations to meet modern challenges.

Best practices for incorporating AI and automation in your SOC include:

Define Clear Goals: Identify specific areas within your SOC where AI can deliver the most impact, such as reducing false positives, automating repetitive tasks, or improving response times.

Start Small and Scale Gradually: Begin by implementing AI in a targeted function, like alert triage or threat detection, before expanding its use across the SOC.

Integrate AI with Existing Tools: Ensure seamless compatibility between AI systems and your current security tools, such as SIEMs, endpoint detection platforms, and threat intelligence solutions, to create a unified ecosystem.

Ensure High-Quality Data: AI relies on accurate and up-to-date information. Establish strong data governance practices to provide reliable inputs for AI decision-making.

Monitor and Refine Performance: Continuously track metrics like mean time to detect (MTTD) and mean time to respond (MTTR) to evaluate AI performance. Use this feedback to improve AI models and workflows over time.

Upskill SOC Analysts: Train your security team to interpret AI insights and work alongside autonomous systems, ensuring collaboration between human expertise and AI efficiency.

Foster Transparency: Implement AI systems that explain their reasoning, so analysts understand and trust the decisions made by autonomous agents.

ReliaQuest AI Agent for Security Operations

To transform your security operations center into a highly efficient, AI-driven system, ReliaQuest GreyMatter is the solution. ReliaQuest has harnessed decades of security operations data to train generative AI and agentic AI models within its GreyMatter platform, making it uniquely suited for customers looking to augment their security operations teams. Pairing these AI capabilities with automation speeds threat detection, containment, investigation, and response even further, resulting in mean times to contain (MTTC) of 5 minutes or less for our customers.