The rise of agentic AI in cybersecurity
Cybercriminals are increasingly leveraging AI and automation to execute attacks at machine speed. These threats continuously adapt, making traditional security approaches ineffective and leaving security operations teams scrambling.
Relying solely on human analysts and static, playbook-driven automation is no longer enough. Cyber threats move faster than manual workflows can handle, demanding a solution that matches their speed, intelligence, and adaptability.
Enter agentic AI—an emerging approach that brings autonomous, decision-making AI into security operations. By mimicking human analysts, adapting to new threats, and streamlining workflows, agentic AI is transforming SOC efficiency and effectiveness.
What is agentic AI?
Before we explain how agentic AI is being applied in cybersecurity, let’s take a step back and define what it is. Agentic AI refers to artificial intelligence systems that act autonomously as “agents,” capable of carrying out tasks and making decisions without human prompting or intervention. Unlike traditional AI models that analyze data or execute pre-defined actions, agentic AI combines advanced frameworks to mimic human decision-making processes, adapting to new challenges and learning from its interactions.
How does agentic AI work in cybersecurity?
For security teams, agentic AI presents a massive opportunity to reinvent how security operations are done. Unlike traditional rule-based automation, which rigidly follows predefined logic, agentic AI learns from security events to improve decision-making over time. These AI-driven systems:
- Perform contextual investigations autonomously, processing large-scale security data without human prompting.
- Make dynamic decisions based on real-time data, reducing reliance on manual analysis.
- Interact with security tools and orchestrate responses across external systems for seamless incident remediation.
Agentic AI operates through a combination of machine learning models, behavioral analytics, and adaptive decision-making. These AI agents continuously analyze data from security logs, identity providers, and external threat intelligence feeds to make informed security decisions.
Key components of agentic AI for cybersecurity
- Context-Aware Analysis: AI agents assess alerts based on historical data, network behavior, and organizational context to determine whether an alert is malicious or benign.
- Automated Evidence Correlation: Unlike traditional automation, agentic AI connects the dots between different security events, reducing the need for manual investigations.
- Self-Learning and Feedback Loops: AI agents improve over time by incorporating feedback from security analysts and adjusting decision-making processes based on emerging threat patterns.
Why agentic AI is needed in cybersecurity
The current cybersecurity landscape presents multiple challenges that make it increasingly difficult for security operations teams to keep up with attackers.
Playbook-driven security automation has fallen short
Security solutions that have adopted AI and machine learning techniques have excelled at identifying anomalies and flagging potential threats, and automated workflows and SOAR platforms have streamlined post-detection actions. However, there are still massive bottlenecks in alert triage and investigation. Today’s solutions still heavily rely on human decision-making to sift through alerts, correlate evidence, and determine if additional action is needed. Traditional, playbook-driven automation tools simply can’t deliver the context, critical thinking, and nuanced decision-making needed to effectively triage and investigate alerts.
Legacy systems aren’t built for AI-powered threats
Many organizations still rely on outdated SIEM, EDR, and firewall systems. While these tools served their purpose in the past, they lack the speed and adaptability to counter AI-driven threats. Cybercriminals now automate their attacks, evading detection and making it impossible for legacy security solutions to respond effectively.
Alert fatigue overwhelms analysts
Security teams process thousands of alerts daily, many of which are false positives. Analysts waste critical time sorting through non-threatening alerts instead of focusing on real risks. Some CISOs report 10,000+ security events per day, yet only a handful represent actual threats.
The cybersecurity talent shortage
There simply aren’t enough skilled analysts to meet the growing demand. Burnout is rampant, and organizations struggle to hire and retain cybersecurity professionals. Agentic AI helps bridge this gap by handling repetitive, high-volume tasks, enabling security analysts to focus on complex investigations.
The data deluge is unmanageable
Security operations must analyze vast amounts of real-time data to detect anomalies and potential breaches. However, the volume of security logs, cloud events, and identity signals far exceeds human capacity. Without automated intelligence to filter and prioritize data, organizations risk missing critical threats.
How agentic AI is transforming security operations
Agentic AI is redefining workflows by eliminating manual bottlenecks and accelerating response times.
Automated alert triage
Security teams are flooded with alerts, many of which turn out to be false positives. Agentic AI processes and prioritizes alerts, dismissing non-threats and flagging high-risk incidents. This reduces analyst workload—freeing security teams to focus on real dangers.
Faster incident response
Speed is crucial when responding to security threats. The longer an attacker remains undetected, the greater the potential damage. Agentic AI significantly reduces mean time to detect (MTTD) and mean time to respond (MTTR) by investigating security events in real time, isolating compromised accounts, and blocking malicious activity before threats escalate.
Continuous learning and improvement
Traditional security automation relies on predefined playbooks that require frequent updates to remain effective. However, agentic AI continuously learns from past incidents, improving its ability to detect and respond to emerging threats. By leveraging large language models (LLMs), analysis tools, and threat intelligence feeds, these AI systems refine their analysis and decision-making capabilities over time.
Improved detection and security posture
Agentic AI can uncover and detect incidents that would usually be ignored. AI is scalable and has unlimited capacity. Therefore, it has the capacity to investigate low-severity—or even informational—alerts that would otherwise be completely overlooked by humans. While these kinds of alerts are often benign, they can sometimes be incidents that get missed simply because humans do not have the time to investigate them.
Real-world cybersecurity agentic AI use cases
Stopping identity-based attacks
Agentic AI autonomously investigates suspicious login activity, such as impossible travel incidents or suspicious access patterns. If compromised credentials are detected, the AI triggers automated remediation steps like forcing a password reset, revoking session tokens, or escalating the issue to human analysts.
Detecting ransomware in its early stages
An MDR provider or an in-house SOC team typically has an SLA of 30 minutes to detect and contain a ransomware incident. An AI agent, on the other hand, can do this within seconds or minutes. This can drastically improve MTTD and prevent devastating, widespread encryption.
Closing cloud security gaps
Agentic AI can seamlessly gather logs, files, and forensic artifacts and correlate alerts from various cloud environments to significantly reduce false positives so security teams can focus on critical threats. AI agents can also enforce security policies with automated mitigation actions and interact with IT teams and end users to eliminate the need for manual intervention.
Managing reported phishing emails
Leveraging agentic AI to monitor reported phishing pipelines can save SOC teams a significant amount of time. AI agents can extract all pieces of evidence from emails, including URLs, attachments, metadata, email content, and more, and then intelligently analyze all the evidence to determine whether the email is malicious and needs to be escalated for further action.
The future of agentic AI in security operations
Adoption of agentic AI is accelerating, with significant investment and innovation shaping the market.
Security technology firms and investors are recognizing the potential of agentic AI to revolutionize security operations. $2B has been invested in agentic AI startups over the past two years (Deloitte).
Market research indicates that the agentic AI industry is set to reach $367.68B by 2033 (Emergen Research), with 33% of enterprise security software predicted to include agentic AI by 2028 (Gartner).
Getting started with agentic AI
Agentic AI is only as effective as the tools and data it can access. Without seamless integration into an organization’s security stack, even the most advanced AI agents risk making poor decisions, acting on incomplete information, or failing to deliver meaningful security outcomes.
Building a strong AI foundation
A successful AI-driven investigation requires more than just automation—it depends on high-quality evidence collection, advanced analysis tools, and strong threat intelligence data. AI agents that rely solely on alert descriptions and a limited set of indicators are doomed to fail.
Security alerts often provide only a high-level summary, lacking the necessary context to determine whether an incident is a real threat or a benign anomaly. Without robust evidence collection, AI risks generating false positives, overlooking critical threats, or even “hallucinating” conclusions based on incomplete data.
Overcoming integration challenges
Many organizations hesitant to adopt agentic AI often worry about compatibility with their existing security ecosystem. The most effective deployment requires a strategic approach to integration:
- Seamless interoperability: AI agents should connect effortlessly with SIEM, SOAR, EDR, and identity management systems, ensuring smooth data flow and enabling comprehensive threat analysis.
- Incremental deployment: Organizations should introduce AI-driven security in phases, starting with specific use cases like alert triage before expanding to broader applications like incident response and threat hunting.
- Human-AI collaboration: AI should augment, not replace, security analysts. The best implementations use AI to handle repetitive, high-volume tasks, freeing human experts to focus on complex threats that require critical thinking and expertise.
By ensuring robust integrations and following best practices, organizations can deploy agentic AI with confidence—enhancing security efficiency, reducing analyst workload, and strengthening defenses against evolving cyber threats.
Cyber threats are evolving too fast for traditional security approaches to keep up. Security teams are overwhelmed, and the talent shortage is only getting worse. Agentic AI offers a solution that scales operations, improves response times, and reduces analyst burnout.
Organizations that adopt agentic AI will gain a significant competitive advantage in cybersecurity, reducing risk exposure and improving their ability to respond to modern threats.
Request a demo to see agentic AI in action.
Intezer Team
