Agentic AI & Cybersecurity: A Powerful Partnership

At its core, automation is designed to execute specific sequences of pre-defined rules in a single flow to drive data-driven decisions. Powerful automation platforms have helped security operations (SecOps) teams optimize their workflows and focus on what is essential. Recent advancements in generative AI and agentic AI demonstrate a lot of promise to solve complex problems and transform day-to-day operations for SOC teams. 

AI’s cognitive capabilities can be leveraged in automation flows to streamline the process and immensely improve decision-making.  In AI-enriched automation, AI provides intelligent insights, analyzes vast amounts of data and patterns, learns from them, and makes informed decisions while executing predefined tasks and processes. When AI is applied within automation, processes can adapt to changing conditions and make decisions without human intervention.  

Keep reading this blog to learn more about the nuances of the different types of AI and how they challenge the notion of what’s possible with security automation.     

What is Agentic AI?

Agentic AI is a skillful coworker; it impersonates one or more skills of an expert, such as a highly trained SOC analyst. Agents operate with a level of autonomy where they can understand the context of the problem, analyze it, and develop strategies to solve it. They can even alter strategy by learning new information or developing new skills. Above all, AI agents can decide whether to act to resolve the problem. 

For example, a cybersecurity AI agent can take context from their environment, seek and augment a piece of information, prioritize a case, remediate a specific alert, or resolve a case. The diagram below provides a visual summary of how agentic AI works in a chat scenario.  

Agentic AI vs. Generative AI: What’s the Difference? 

The buzz around AI often blurs the lines between generative and agentic AI, yet their core functionalities diverge significantly. ChatGPT is an excellent example of generative AI, which excels at creating novel content—text, images, or code—based on learned patterns. Think of it as a highly skilled artist, capable of producing stunning outputs from provided prompts. Many cybersecurity teams adopt generative AI capabilities for tasks like summarizing cases or writing code. 

Agentic AI, however, takes a more autonomous role. It’s designed to perceive, reason, and act within an environment to achieve specific goals. Instead of simply generating content, agentic AI can execute tasks, make decisions, and adapt to changing circumstances, essentially operating as an intelligent, self-directed agent. Understanding this distinction is crucial for teams implementing AI across their security operations. 

Agentic AI in Cybersecurity: The Need and Potential 

Despite the risks, agentic AI holds significant potential to address pressing SOC challenges. Its autonomous nature allows for real-time threat detection and incident response, surpassing the limitations of human analysts and traditional security systems. For example, agentic AI can continuously monitor network traffic, identify subtle anomalies indicative of advanced persistent threats (APTs), and automatically initiate containment measures before significant damage occurs.  It can also automate vulnerability management by proactively scanning for weaknesses, prioritizing remediation efforts, and applying patches in a controlled environment. By leveraging its ability to learn and adapt, agentic AI can stay ahead of evolving threats, ultimately bolstering an organization’s security posture. 

Top Benefits of Agentic AI for SecOps 

• Enhanced threat detection & response 
• Automated vulnerability management 
• Improved security orchestration automation and response (SOAR) 
• Enhanced threat intelligence and analysis 
• Improved security posture management 
• Reduced alert fatigue 
• Autonomous incident containment 

Top Ways to Use Agentic AI in Cybersecurity 

When applied in concert with the guardrails of automation and humans in the loop, agentic AI can transform many standard SecOps processes. There is no one-size-fits-all regarding how agentic AI is applied within cybersecurity because every organization’s environment, tools, AI policy, and overall SecOps maturity are unique. With this in mind, here are a few example scenarios of use cases that early adopters are starting to use agentic AI & automation. 

AI Automation for Incident Response 

AI agents transform incident response by autonomously analyzing large datasets in real time to identify and contain attacks. Rather than just alerting, they execute pre-configured or dynamically generated response actions, such as isolating endpoints or modifying firewall rules. This automation minimizes damage and dwell time, shifting security teams from reactive firefighting to proactive defense.

AI Automation for Vulnerability Management 

Agentic AI transforms vulnerability management from reactive scanning to proactive risk mitigation by continuously monitoring for vulnerabilities and autonomously assessing their severity. It then dynamically prioritizes patching efforts, focusing on the most critical weaknesses and automating remediation in controlled environments, significantly reducing an organization’s attack surface and improving its security posture. This intelligent approach minimizes alert fatigue and streamlines the overall vulnerability management process.

Improve SIEM Alert Traige with AI Automation 

Agentic AI automates SIEM alert triage by analyzing and prioritizing security events, reducing the flood of raw alerts for analysts. These AI agents correlate events, filter out false positives, and provide concise summaries of critical incidents, minimizing alert fatigue. By intelligently prioritizing alerts based on risk, security teams can focus on the most pressing threats and improve overall efficiency and security posture. 

Threat Hunting with AI Automation 

Agentic AI systems support threat hunting by learning normal network behavior and autonomously investigating deviations like data exfiltration. Instead of simply alerting, they trace anomalies, correlate intelligence, isolate systems, and uncover hidden threats. This proactive approach provides analysts with actionable insights and speeds up attack containment.

Challenges and Considerations Before Getting Started 

Before integrating agentic AI into SecOps, teams must navigate a complex landscape of challenges and considerations. 

• Ethical implications are paramount, requiring careful deliberation on autonomous decision-making and potential biases within AI models. 
• Building trust hinges on explainability; understanding how AI agents arrive at their conclusions is crucial for human oversight and accountability. 
• Seamless integration with existing security infrastructure is another hurdle, demanding robust APIs and data standardization. 
• Rigorous model training and validation are essential to ensure accuracy and prevent unintended actions, while continuous monitoring is needed to adapt to evolving threat landscapes. 
• Clear AI governance frameworks are great tools to help SecOps teams address data privacy concerns and develop comprehensive incident response plans for AI-driven security events.

The Future of AI Automation

To summarize, the future of cybersecurity and AI is exciting. The combination of AI decision-making and dynamic reasoning capabilities, paired with automation actions at scale, are the capabilities security teams need to tackle their toughest challenges. To learn more about Swimlane’s AI automation capabilities, visit https://swimlane.com/swimlane-turbine/