Agentic SOAR is security orchestration, automation, and response technology that uses AI to autonomously evaluate threats, make informed decisions, and initiate responses in real time without human intervention.
Table of Contents
Traditional SOAR was designed to reduce workloads for security operations centers (SOCs). It integrates with security information and event management (SIEM), endpoint security, and other security tools, using automation to initiate responses based on prebuilt playbooks. While SOAR’s automation increased efficiency, it also created challenges for security teams, including:
- High alert volumes, false positives, and triaging requirements that eat up analyst time
- Labor-intensive playbook maintenance
- An inability to react dynamically to emerging attacks
Agentic SOAR goes a step beyond traditional SOAR. It allows organizations to move from static playbooks to a dynamic, autonomous system that makes intelligent decisions based on contextual understanding. It investigates threats, triages them, and chooses the appropriate containment response, all without human intervention.
How agentic SOAR is transforming security operations
As mentioned, one of the limitations of traditional SOAR is that it operates on static playbooks that require manual updates to respond to new or emerging threats. This reduces its effectiveness in complex scenarios that require reasoning or decision making. Even when using traditional SOAR, analysts are still required to intervene, especially when it comes to investigation, triage, or edge cases.
Agentic SOAR uses reasoning-driven investigation to analyze and triage threats, make decisions, and adapt without human intervention. Alerts first go to AI agents instead of human analysts. The agents use large language models (LLM), historical and behavioral context, external data like threat intelligence feeds, and a series of tests to classify the severity of the alert. They then produce a readable, detailed report of their findings and reasoning. Only at that point does an analyst need get involved to review the findings. And in some cases, agentic SOAR can undertake remediation actions without any manual intervention at all.
Key features of agentic SOAR
What sets agentic SOAR apart is its autonomy and sophisticated reasoning. The system is characterized by:
- Learning and reasoning—The system is designed to learn continuously from every event, giving it contextual memory. It uses machine learning and LLM to build its logic and explain its decision-making process.
- Autonomous triage—AI analyzes and prioritizes threats by applying contextual understanding, undertaking dynamic investigations, and synthesizing data from multiple sources to draw conclusions and implement or recommend actions.
- Real-time threat response—Agentic SOAR dynamically creates and changes response protocols in real time based on the data it uncovers.
- Integration—Agentic SOAR integrates with the organization’s existing security solutions, interacting seamlessly with tools such as endpoint detection and response (EDR), SIEM, and cloud platforms.
- Multiple agents—Each AI agent is trained for a particular stage of investigation, triage, or response, ranging from intelligence gathering and risk assessment to sharing and collaboration.
- User-friendly interface—The use of natural language processing makes it easy for analysts to prompt agents, and for analysts to understand the agent’s reasoning.
Advantages of agentic SOAR
While traditional SOAR has been a great advancement for the SOC, it has its constraints. By comparison, the benefits of agentic SOAR include:
- Rapid response times—Significant improvements in mean time to detection and mean time to response.
- Risk reduction—Greater accuracy in correctly identifying and classifying threats, and higher detection of potential threats that might otherwise be missed.
- Increased productivity and morale—Optimization of operations and human resources with analyst time being diverted from managing alerts to strategic considerations.
- Scalability—The ability to respond to emerging attacks and manage a growing attack surface without the need for new resources.
- Continuous learning—Ongoing acquisition of knowledge, building up a knowledge base specific to the organization and industry.
Best practices for implementing agentic SOAR
As with any new technology, there are hurdles in implementing agentic SOAR. Since AI is in control of decisions, actions, governance, oversight, and reliability, it can present unique challenges. Security and privacy are also concerns because AI needs to have access to large amounts of sensitive data. There may also be issues integrating agentic SOAR with legacy systems.
With these challenges in mind, here are some best practices for implementing agentic SOAR:
- Assessment—Determine current needs and current SOAR maturity. Review approaches based on the organization’s needs and the proven results of the solutions. Consider a pilot program.
- Governance—Create clear oversight for autonomous decisions. Outline roles, responsibilities, and ethical guidelines.
- Human-in-the-loop—Ensure analysts continue to be involved, reviewing and monitoring.
- Security and compliance—Institute robust encryption, access controls, and regular vulnerability assessments.
- Testing and validation—Set success metrics for assessing effectiveness. Undertake regular, thorough testing and review.
Prepare for the future with agentic SOAR
With cyber criminals leveraging AI to create more sophisticated attacks, organizations need to embrace the power of agentic technology in the SOC. Agentic SOAR will transform security operations by increasing the accuracy of threat detection, speeding up containment, and reducing the burden on human beings. This will allow analysts to focus on strategic activities like threat hunting, analyzing risk trends, and developing broader, cross-functional skills.
However, security teams should not think in terms of having to choose between agentic or human solutions. The most successful organizations will be those who adopt a hybrid approach, using AI to enrich event management while keeping a human in the loop to review and make final decisions.
Where can I get help with agentic SOAR?
Using the right technology is critical. Trend Vision One™ Agentic SOAR enables your team to move beyond static playbooks into a fully AI-driven SOC that investigates, triages, and responds in real time. Combining AI-powered investigations, end-to-end SOC automation, a connected ecosystem, and natural language playbook creation, you can reduce manual workloads and empower your security team to focus on strategic priorities without drowning in alerts.
Jayce Chang
Vice President of Product Management
Jayce Chang is the Vice President of Product Management, with a strategic focus on Security Operations, XDR, and Agentic SIEM/SOAR.
Frequently Asked Questions (FAQs)
What does agentic mean?
Agentic comes from the word “agency” which means the power to act. Agentic SOAR therefore means a SOAR solution that can act independently.
What is agentic behavior?
Agentic behavior describes the ability of artificial intelligence systems to make decisions, act, and adapt to environmental changes without human intervention.
What do you mean by SOAR?
SOAR stands for security orchestration, automation, and response and refers to a cybersecurity solution that integrates security tools and automates tasks, making security operations more efficient.
What does SOAR stand for?
The acronym SOAR stands for security orchestration, automation, and response.
What are examples of agentic behavior?
Examples of agentic behavior include a digital assistant scheduling alarms without user prompting, a self-driving car choosing a driving route, or an IT system rerouting traffic.
What is an example of agentic learning?
An example of agentic learning is a virtual assistant that notices repeated actions, meetings, and locations of the user and automatically sets alerts for them.
What are the top three agentic frameworks?
There are many agentic frameworks. The three referred to most often are Microsoft AutoGen, CrewAI, and LangGraph.
What is the meaning of agentic workflow?
An agentic workflow is the process used by an AI agent to autonomously gather information, choose between options, and initiate a task without human intervention.
What is an example of an agentic workflow?
An example of an agentic workflow in cybersecurity would be having an AI agent autonomously inspect a security alert, correlate data from various sources, and then choose and initiate a containment action.
What is the difference between workflow and agentic?
A workflow is a predetermined series of tasks. An agentic system is made up of an autonomous AI that can choose which actions best suit the context.