Agentic SIEM is an artificial intelligence system designed to autonomously perform security information and event management (SIEM) without human intervention.
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As the rate and sophistication of cyberattacks increase, it is more challenging for security operations centers (SOCs) to manage the large number of security alerts being generated. The introduction of security information and event management (SIEM) helped ease the workload. It’s a system that collects, analyzes, and correlates security data from across an organization to detect threats and support incident response. Now, agentic SIEM goes one step further by using AI to evaluate large volumes of data, adapt dynamically to changing conditions, and make informed decisions to achieve an organization’s security goals.
It’s called ‘agentic’ because it is made up of interconnected, autonomous AI components called agents.
Agentic security information and event management (SIEM) can be trained to:
- Examine inputs from log data, user behavior, identity, and cloud
- Apply reasoning to scenarios like multiple failed login attempts or unusual network traffic
- Make informed decisions to isolate a suspicious endpoint, temporarily suspend an account, or generate a prioritized incident ticket
- Initiate response plans
- Undertake investigations
- Constantly learn
What’s the difference between agentic SIEM and traditional SIEM?
Traditional SIEM solutions require human analysts to evaluate and respond to the alerts the system generates. This is workable for a limited number of alerts per day but fails at scale. Agentic SIEM applies AI and machine learning to handle massive volumes of alerts.
Traditional SIEM is essentially an advanced log aggregator, while agentic SIEM is like an intelligent analyst with an excellent memory. Agentic SIEM makes decisions dynamically based on history and context and learns from the patterns it sees, choosing the most efficient path to resolution through application programming interfaces (APIs).
Machine learning is crucial to agentic SIEM. AI agents observe the organization to learn the history of security decisions. They look at how engineers write rules, respond to threats and patterns, react to false positives, and adjust thresholds. By detecting the thought process behind every action, agentic SIEM learns to make intelligent decisions.
How does agentic SIEM work?
Agentic SIEM gathers information from multiple sources for real-time analysis, including cloud environments, endpoints, user and device identities, attack patterns, recent system changes, regulations, and more.
It then carries out automated tasks through APIs, creating summaries of its actions and explanations for its choices. These stored ‘pathways’ can then be referenced by AI agents and human analysts to enhance future decisions.
Agentic SIEM applies reasoning based on large language models (LLMs), draws on its ever-growing memory, and processes new information to inform its decision-making. When it comes to running investigations, agentic SIEM acts dynamically, pivoting its path based on information it uncovers rather than being restricted to a narrow checklist.
Key benefits of agentic SIEM
With its independence, intelligence, and memory, there are extensive benefits to agentic SIEM:
- Enhanced threat detection and response capabilities—Since agentic SIEM is continuously monitoring the environment, it can spot advanced persistent threats (APTs), allowing organizations to identify issues in real time. The system then automates containment measures, speeding response time and reducing damage.
- Proactive threat hunting—Agentic SIEM scans for and prioritizes vulnerabilities constantly, helping the organization stay ahead of malicious actors.
- Intelligent analysis to reduce false positives—Thanks to its large database of contextualized decisions, agentic SIEM draws more accurate conclusions about potential threats, leading to fewer false positives. This reduces alert fatigue and increases the efficiency of security operations.
- Scalability and adaptability for evolving security needs—Agentic SIEM learns continuously, making security operations more agile. This lets organizations be more proactive in the face of evolving threats, refining responses based on previous outcomes and improving their security posture without human intervention. Analyst time is redirected from managing alerts to more strategic tasks.
Real-world applications of agentic SIEM
Almost any industry can benefit from implementing agentic SIEM. Here are a few examples:
- Managed security—When they deploy agentic SIEM, managed security service providers (MSSPs) can reduce the number of false positives they have to handle, auto-close tickets, proactively surface non-obvious alerts, and deliver verdicts more quickly. This results in a reduction of the analyst’s effort and time to triage, both of which translate into improved customer service and reduced costs.
- Manufacturing—Agentic SIEM allows manufacturing organizations to correlate security events across business applications, production systems, and user activity. This enables them to identify suspicious behavior patterns more quickly and initiate containment actions without manual intervention. The result is a higher rate of mitigation, a reduction in time spent on security incidents, and a decrease in downtime.
- Financial services—With large amounts of sensitive data and distributed infrastructure, the stakes are high in financial services. Agentic SIEM can triage, escalate, and coordinate responses automatically between security and IT teams. This reduces mean time to acknowledge and mean time to respond, improving uptime and accelerating incident handling.
Challenges and considerations in implementing agentic SIEM
While the benefits of agentic SIEM are far-reaching, it has its challenges, including:
- Accountability—Clarifying who is responsible for actions and outcomes since the agentic SIEM system runs independently and makes its own decisions
- Oversight—Determining the appropriate level of human involvement
- Data privacy—Establishing data governance to reduce security risks and comply with regulations
- Ethical governance—Setting clear boundaries for decision-making and ensuring transparency
Organizations should implement agentic SIEM carefully and gradually with the following in mind:
- Align AI systems with business and security objectives
- Integrate with existing security infrastructure by implementing robust APIs and data standardization
- Create well-defined roles for agent and analysts
- Set up fallback mechanisms to easily override agent decisions
- Implement meticulous agent training, particularly on domain-specific expertise
- Establish robust documentation and audit trails of agent learning and decision-making to ensure trust in the system
- Set up continuous monitoring to adapt to evolving threat landscapes
The future of security operations with agentic SIEM
As agentic SIEM becomes more widespread and sophisticated, the nature of security operations will evolve. One of the biggest changes will be in the role of analysts. They will be able to delegate everyday tasks and triage to agents and move from running reactive investigations to evaluating AI-driven ones. This will free up their time to focus on proactive threat hunting and strategic decisions. The shift isn’t a matter of delegating everything to a machine, though. It’s about establishing a careful balance between agent functionality and human intelligence.
Where can I get help with agentic SIEM?
SOC teams often struggle to optimize their SIEM due to limited resources and heavy-manual effort required, leaving them with an overwhelming amount of data but little actionable insight. With traditional SIEMs being reactive by design, your SOC team is not able to act fast and focus their attention.
Trend Vision One™ Agentic SIEM, part of the Trend Vision One™ Security Operations (SecOps), treats your schema like a language. Using AI to understand the intent behind the data, supporting native and third-party sensors and over 900+ third-party data sources, you can proactively reduce risk, automate responses, and maximize the value of your existing security investments.
Jayce Chang is the Vice President of Product Management, with a strategic focus on Security Operations, XDR, and Agentic SIEM/SOAR.
Frequently Asked Questions (FAQ's)
What are the three characteristics of SIEM?
The three characteristics of SIEM are: 1) real-time data and log collection and correlation; 2) real-time alerts and notifications; 3) the use of AI to provide prioritization, alerts, and reporting.
What are the three different types of SIEM tools?
SIEM tools can be on-premises (installed on the organization’s server), cloud (hosted by a cloud provider), and hybrid (a combination of both).
What is the difference between SIEM and next gen SIEM?
While SIEM includes automation based on pre-defined rules, next-gen SIEM includes AI, machine learning, and advanced automation, making it able to resolve issues faster and proactively detect threats.
What is Google’s SIEM tool called?
Google’s SIEM tool is called Google Security Operations. It includes cloud-based SIEM, a unified platform, scalable infrastructure, and threat intelligence.
What are the frameworks for agentic workflows?
Frameworks for agentic workflows comprise a set of tools and structures for building autonomous AI agents for complicated, multi-step tasks.
What are the most used agentic frameworks?
The most used agentic frameworks are LangChain, LangGraph, and Microsoft AutoGen.
What is agentic AI security?
Agentic AI security uses autonomous AI agents to make decisions and initiate responses to security threats, with careful monitoring.
What is agentic AI technology?
Agentic AI technology is an autonomous artificial intelligence system that is trained to accomplish a particular goal with little need for human oversight.
What are the risks of agentic AI?
Agentic AI can present risks such as data vulnerability, ethical considerations, limited control, or misuse.
Is agentic AI real?
Yes. Autonomous artificial intelligence (AI) systems exist that make decisions and take actions without human intervention.