Agentic AI is reshaping the future of security operations by enabling autonomous agents to tackle complex challenges and independently resolve incidents. In this blog, we’ll break down the key differences between agentic AI and generative AI, explore the role of multi-AI-agent systems in security, and examine how agentic AI frameworks are driving advancements in security operations. You’ll also learn how generative AI has impacted the cybersecurity landscape and why multi-agent AI systems will become essential tools in modern defense strategies.
What Is Agentic AI?
Agentic AI refers to an advanced artificial intelligence architecture designed to perform tasks autonomously. It leverages generative AI to interpret data, make informed decisions, and execute actions without human intervention, making it especially valuable in high-stakes environments such as security operations, where speed and accuracy are paramount. By integrating generative AI models, agentic AI systems can respond swiftly and decisively to security threats.
These AI systems are often embedded into security operations software and hardware, working alongside human operators to enhance overall effectiveness. By automating routine tasks and providing real-time threat detection and response, agentic AI helps security teams focus on more strategic activities.
Key Characteristics of Agentic AI:
- Autonomy: Executes tasks without human intervention.
- Real-Time Response: Swiftly reacts to security threats.
- Generative Capabilities: Utilizes generative AI to enhance decision-making and actions.
What Is an AI Agent?
AI agents are the building blocks of agentic AI, with each agent designed to autonomously perform specific tasks with precision. When multiple agents work together, they form a multi-AI-agent system, where each agent handles a distinct job under the guidance of an orchestrator. This collaboration enables more complex, scalable, and efficient operations.
For example:
- One agent monitors network activity to detect anomalies in real time.
- Another automates routine tasks like patch management or system updates.
- A “manager” agent orchestrates these actions so that the agents work together as a unified system.
What Is an AI Agent Framework?
An AI agent framework provides the foundation for deploying and managing multiple AI agents. It facilitates communication between the agents, enabling them to share information and coordinate their actions seamlessly. In security operations, these frameworks allow organizations to implement multi-agent security operations systems that can:
- Detect and respond to threats in real time. Automate routine tasks like alert triaging and filtering false positives.
- Provide in-depth analysis for decision-making.
Applications of Agentic AI in Security Operations
Agentic AI is widely used in automated threat detection systems that analyze network traffic and respond to anomalies instantly. These systems are embedded into security operations platforms, continuously monitoring network activity to identify potential threats.
SOC Automation
In security operations centers (SOCs), agentic AI plays a crucial role in automating processes and workflows, including alert enrichment, data collection, and contextualization. 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—and agentic AI excels by acting immediately to contain the threat. By doing so, it prevents the threat from spreading and causing further damage. For example, if a malware infection is detected, the AI system can isolate the affected devices, preventing the malware from propagating across the network.
In addition to containment, 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 ensures that security incidents are addressed promptly and efficiently, minimizing the effort needed from an analyst and neutralizing disruption to business operations.
Threat Hunting
Agentic AI can significantly enhance threat hunting by autonomously scanning networks for indicators of compromise (IOCs). Utilizing generative AI models, it can identify patterns and anomalies that might be missed by traditional methods. This proactive approach allows security operations teams to uncover hidden threats and vulnerabilities before they can be exploited. By continuously learning from new data, agentic AI improves its threat detection capabilities over time, making it an invaluable tool for maintaining a robust security posture.
Risks of Agentic AI in Security Operations
While agentic AI offers numerous benefits, it also comes with potential risks:
Black-Box Decision-Making: Agentic AI often operates in a “black box,” making decisions that security operations teams may not fully understand. This lack of transparency can hinder troubleshooting and strategy adjustments, as operators find it difficult to assess the rationale behind AI-driven responses.
Misinterpretation of Data: AI systems can misinterpret data, resulting in false positives. For instance, legitimate network traffic might be wrongly flagged as malicious, leading to unnecessary alerts and disruptions. These false positives consume valuable time and resources and can cause alert fatigue.
Hallucination: Without access to the right information, some AI models, including agentic AI models, will create outputs based on nothing—a phenomenon known as hallucination.
Human Complacency: The efficiency of agentic AI can lead to over-reliance, causing human operators to become less vigilant. This complacency can erode the skills and readiness of security operations teams, leaving them unprepared for complex or unexpected threats that the AI may not handle effectively.
What Is Generative AI?
Generative AI refers to artificial intelligence models that create new content by identifying and replicating patterns in the data they were trained on. It leverages machine learning, such as large language models (LLMs), to generate outputs that resemble the training data. These models excel at tasks like generating text, creating images, and even synthesizing code.
Key Characteristics of Generative AI:
- Creativity: Generates unique outputs based on training data.
- Data Synthesis: Combines information from various sources to create coherent content.
- Versatility: Applicable across numerous domains, from marketing to cybersecurity.
Applications of Generative AI in Security Operations
Generative AI can be leveraged in various ways to strengthen security measures. Its ability to generate new data and information makes it a valuable tool for enhancing threat intelligence and improving incident response capabilities.
Summarizing Alert Tickets
One of the primary applications of generative AI in security operations is helping collect and synthesize alert data. If an alert for the same incident fires on multiple tools, generative AI can analyze the similarities and differences in the alert data between tools and present a clear summary to a human analyst, who can then act on the information.
Data Analysis
Generative AI excels at identifying patterns and anomalies, proactively detecting potential threats. By continuously learning from new data, AI improves its detection capabilities, helping maintain a robust security posture. For instance, AI can analyze network traffic to flag unusual activities that might indicate an attack.
Improving CLI Interpretation
Generative AI can enhance command line interface (CLI) productivity by providing auto-completion and contextual suggestions. As a user types, the AI predicts and completes commands, offering suggestions based on context and historical usage patterns. Generative AI can also help detect and correct command errors in real time. When a user enters an incorrect or incomplete command, the AI can identify the error, suggest corrections, and even automatically correct the command if the intent is clear.
Risks of Generative AI in Security Operations
False Positives: Generative AI can sometimes generate inaccurate or misleading results based on patterns they have learned from training data. For instance, an AI might mistakenly flag legitimate network activity as malicious, leading to unnecessary alerts and potential disruptions.
Manipulation: Adversaries could potentially exploit generative AI models to create deceptive content, such as deepfakes or fabricated threat intelligence.
Validation Challenges: Ensuring the accuracy and integrity of AI-generated outputs can be difficult. Generative AI models rely on vast amounts of data and complex algorithms, which can make it challenging to validate the results they produce. Without rigorous validation processes, AI systems may produce outputs that are not aligned with the actual security context, leading to ineffective or even harmful actions.
To mitigate these risks, it’s essential to implement rigorous validation and verification processes. These measures ensure the accuracy and integrity of AI-generated outputs, providing security operations teams with reliable and actionable insights. Organizations should establish comprehensive validation frameworks that include regular audits, cross-validation with external data sources, and continuous monitoring of AI performance. By doing so, they can build trust in AI technologies and ensure that they are used responsibly and effectively.
Agentic AI vs. Generative AI: What’s the Difference?
While both agentic AI and generative AI leverage artificial intelligence to handle complex tasks, their methodologies, strengths, and applications differ significantly. Understanding these distinctions—and how generative AI integrates into an agentic AI architecture—is crucial for optimizing security operations. While generative AI enhances agentic AI by providing data synthesis and insights, each serves distinct purposes within a security operations framework.
| Agentic AI | Generative AI | |
|---|---|---|
| Strengths | Autonomous decision-making for real-time threat detection and response; automation of routine tasks | Creativity to generate insights based on data; data summary; and synthesis |
| Ideal for | High-stakes, real-time security operations; automating SOC processes | Data collection and interpretation |
| Not ideal for | Tasks requiring human intuition and creativity | Real-time, autonomous threat response |
| Risks | Opaque decision-making; false positives based on misinterpreted data; hallucination | Generation of inaccurate or misleading results; validation challenges |
The Next Iteration of Agentic AI—Multi-AI-Agent Systems
As AI technology advances, the concept of AI “super agents” or “multi-AI-agent systems” is set to be a game-changer. These sophisticated systems are composed of multiple specialized agents working collaboratively under an orchestrator agent to tackle complex challenges. Beyond being equipped with the tools necessary for their designated roles, these agents can also dynamically create or interact with other specialized agents to achieve their objectives. In security operations, multi-AI-agent systems offer several key advantages:
Distributed Threat Analysis: Multi-AI-agent systems can break down and analyze threats from multiple perspectives, ensuring no critical detail is overlooked.
Coordinated Response Strategies: By working together, agents can execute well-orchestrated responses to neutralize threats with maximum efficiency.
Enhanced Scalability and Efficiency: Multi-agent systems can manage the growing complexity of modern networks with consistent performance.
Applications of Multi-Agent Systems in Security Operations
Multi-agent systems are redefining how security operations are managed by enabling collaboration between specialized AI agents. These systems bring enhanced scalability, precision, and adaptability to security operations, addressing the growing complexity of modern cyber threats.
Focused Expertise
One of the standout benefits of multi-AI-agent systems is their ability to specialize. Current AI agents often operate as a single, generalized entity. In contrast, the specialized agents that make up a multi-AI-agent system each excel at a specific job and are coordinated by an overarching orchestrater agent (think of it as a “SOC director” agent). This approach ensures precision and efficiency, creating a more comprehensive and cohesive defense strategy. For example:
- An IR Analyst agent processes an alert, investigates the initial IOCs, and determines whether escalation is needed. If malicious activity is detected, it coordinates with an Automation Engineer agent to initiate remediation actions.
- A Threat Analyst agent enriches the alert with threat intelligence, generating a detailed report with insights into the attacker’s tactics, techniques, and procedures (TTPs). The agent then passes this information to the next agents for further action.
Minimizing the Risk of Misinterpreting Data
Data misinterpretation poses a significant challenge in security operations, especially given the complexity and volume of data that modern systems must process. Traditional AI systems, which rely on a single model to make sense of all inputs, are more prone to errors if they misread or misprioritize key information. Multi-agent systems mitigate this risk by distributing data processing across multiple specialized agents, with each one focused on interpreting a specific type of data. Collaboration between agents adds an extra layer of accuracy and reliability to the decision-making process through cross-validation.
ReliaQuest AI Agent for Security Operations
ReliaQuest leverages agentic AI to empower security operations teams with autonomous threat detection and response capabilities, ensuring robust and efficient security operations without sacrificing visibility.
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.
