Security operations teams are at breaking point. Analysts are drowning in alerts, detection engineers are stuck in an endless cycle of tuning rules, and proactive threat hunting has become a luxury rather than a priority. Meanwhile, cyber attacks are evolving, growing faster, smarter, and more sophisticated.
To keep up, teams need to rethink how they operate. The solution isn’t to add headcount—it’s to work smarter. Today, generative AI helps security teams with simple tasks like summarizing incident reports. Agentic AI— AI that thinks, acts, and learns autonomously—is already handling security incidents end-to-end. (For an in-depth discussion of generative vs. agentic AI, check out our blog on the subject.)
But now the next evolution of agentic AI is emerging: a system of orchestrated AI agents that work together to solve problems and take action faster and more consistently.
In this blog, we’ll explore:
- What multi-agent systems are and how they build on the foundation of agentic AI.
- How multi-agent systems could transform security operations, with concrete examples of their potential in threat detection and response.
- What’s next for AI in cybersecurity, including addressing common concerns such as workforce impact.
Multi-Agent Systems: The Next Big Thing in AI for Security Operations
The next iteration of agentic AI is multi-agent systems, sometimes referred to as “super agents.” Think of it like a team within your organization, made up of individuals hired to do specific jobs and a single manager who oversees strategy and delegates tasks to the individuals.
This team of agents, led by an orchestrator, synthesizes massive amounts of data, shares information, analyzes options, and executes solutions with precision, combining their advanced capabilities into one system.
Many companies are starting to use AI agents to perform a job made up of multiple tasks. For example, ReliaQuest uses AI agents for things like alert investigation and incident response, leveraging their ability to think and act autonomously to free up human analysts for higher-priority work.
Multi-agent systems, though, are built to handle complex, multi-faceted workflows. Each agent is not only equipped with tools to perform a variety of tasks but can also interact with—or even dynamically create—other specialized agents to achieve broader objectives set by the orchestrator.
How AI Multi-Agents Could Redefine Cybersecurity
A multi-agent system embedded in a security operations platform might consist of multiple autonomous AI agents that perform specific roles—such as an IR analyst agent, a detection engineer agent, or a threat hunter agent, all managed by an orchestrator agent or “SOC director.”
These agents could collaborate with one another to handle the full spectrum of threat detection, containment, investigation, and response workflows.
A Day in the Life of a Multi-Agent System for Security Operations
Imagine your security operations platform receives an alert about a suspected phishing email. A multi-agent system, mirroring the roles of a modern SOC team, springs into action. Here’s how it could work:
- The orchestrator agent acts as the SOC Director, overseeing the entire investigation. It begins by breaking down the phishing alert into smaller, actionable tasks and delegating them to specialized agents. It assigns responsibilities across the team, ensuring each agent focuses on its area of expertise while continuously monitoring progress to adapt the workflow as needed.
- The IR Analyst agent takes the lead in processing the phishing alert. It investigates the initial indicators, such as the sender’s email address and links within the email, and determines whether the alert requires escalation. If malicious content is detected, the agent coordinates with the Automation Engineer agent to isolate affected endpoints and block further communication from the suspicious sender.
- The Threat Analyst agent dives into threat intelligence to enrich the alert. It cross-references the phishing email’s details—such as URLs or file hashes—against known Indicators of Compromise (IOCs) from threat intelligence feeds. It generates a detailed report with insights into the attacker’s tactics, techniques, and procedures (TTPs) and passes this information to the next agents.
- The Detection Engineer agent acts on findings from the IR Analyst and Threat Analyst agents. It updates detection rules to identify similar phishing attempts in the future. For example, it might deploy a rule to monitor emails with similar patterns or block malicious domains identified by the Threat Analyst agent.
- Using the enriched data, the Threat Hunter agent proactively searches for other instances of phishing attempts across the organization’s network. It focuses on identifying patterns that could indicate whether this email is part of a larger campaign. For example, it might uncover multiple employees targeted by emails from the same sender or links leading to similar malicious domains.
- The Risk Analyst agent evaluates the overall risk posed by the phishing alert. It prioritizes actions to minimize exposure, such as recommending employee training for those targeted by the phishing email. It also feeds the findings back to the Threat Hunter agent to validate ongoing hunts or refine the focus for future ones.
- Throughout the process, the Automation Engineer agent designs and executes workflows to automate repetitive tasks, such as blocking malicious domains, isolating compromised endpoints, or notifying employees of the phishing attempt. This ensures the entire system operates efficiently and responds in near real-time.
How It All Comes Together
The orchestrator agent ensures that all agents work in unison to investigate and resolve the phishing alert with precision and speed. For instance, if the Threat Hunter agent uncovers additional compromised accounts, the orchestrator dynamically assigns tasks to the IR Analyst and Automation Engineer agents to remediate the issue.
By combining their specialized capabilities, the agents create an efficient, scalable, and adaptive system that mirrors the workflows of a SOC team. Unlike humans, though, this team operates at machine speed.
Addressing the Elephant in the Room
It’s impossible to have a conversation about AI in cybersecurity without addressing fears around job replacement. But let’s clear this up: AI doesn’t replace people—it amplifies your team’s ability to focus on what truly matters: securing your organization.
The cybersecurity talent shortage is well-documented—you’re probably experiencing it firsthand—and security operations teams are already stretched thin. AI—from generative, to agentic, to multi-agentic—relieves the burden of mundane security operations tasks, allowing analysts to focus on long-term security posture.
The Time for Smarter Security Is Now
Organizations can no longer afford to ignore the transformative potential of AI in cybersecurity. The question is no longer whether AI should be adopted, but how to implement it effectively. Agentic AI and autonomous multi-agent systems are critical for helping security operations outpace attackers, reduce workforce burnout, and empower teams to focus on proactive strategies that strengthen defenses.
At the end of the day, an efficient security operations team is an effective one. So, security leaders, ask yourself: Wouldn’t you agree that giving your team more time for proactive security rather than drowning in alerts is a better use of their time?
How ReliaQuest Uses AI Today
To help organizations combat threats and move faster, ReliaQuest has built a self-learning AI model trained on decades of incident response data within its GreyMatter security operations platform. GreyMatter gives customers full control over training their own self-learning AI agents tailored specifically to their unique security operations environment. By leveraging their own data, historical alerts, and real-time context, customer can fine-tune the AI to address their specific challenges, adapt to evolving threats, and optimize their workflows.
ReliaQuest is already empowering organizations to dramatically improve their security operations using AI agents. And by pairing AI capabilities with automation, GreyMatter helps cut containment times below 5 minutes and frees teams from repetitive Tier 1 and Tier 2 security tasks—paving the way for even greater innovation in the future.
