Agentic AI promises to revolutionize a wide range of IT operations and services, including cybersecurity. While the technology, which accomplishes specific tasks with no human supervision, may seem intimidating to some CISOs, a growing number of cybersecurity leaders are discovering that agentic AI is less complex and easier to deploy than they initially believed.
“As agentic AI becomes more mature, its potential in cybersecurity is particularly compelling,” says Sandra McLeod, CISO at Zoom, noting that many cybersecurity use cases are a strong fit for AI because they take advantage of the technology’s ability to operate at a scale and speed human teams can’t match.
AI can process massive volumes of data continuously without experiencing fatigue, making it ideal for monitoring environments where human attention would eventually taper off, McLeod explains. “It’s especially useful for addressing problems that are either too large in scope or too low in priority for already-stretched security teams,” she says.
Additionally, AI’s ability to respond in real-time means it can act much faster than humans, helping to reduce the blast radius of an attack or minimize the time a threat remains undetected. “By handling high-volume or time-sensitive tasks, AI allows humans to focus on more strategic, higher-value work,” McLeod adds.
Is your organization ready to add agentic AI to its cybersecurity arsenal? Here are seven top use cases for your consideration.
1. Autonomous threat detection and response
A standout use case for agentic AI in cybersecurity is autonomous threat detection and response, which offers the ability to detect, protect, contain and recover from threats at unprecedented speed and scale, says John Scimone, president and CSO at Dell Technologies.
“This includes spotting and disrupting intrusion attempts autonomously in real-time by making security and IT changes to mitigate risks,” he explains. “Essentially, agentic AI can operate as a real-time, autonomous cyber defense agent.”
Cyberattacks are increasingly executed by autonomous agents operating at the speed of light, far outpacing human response capabilities, Scimone says. The primary value of autonomous threat detection lies in speed and scale — two critical factors where traditional methods fall short. “Agentic AI will level the playing field by enabling defenders to respond with equal speed and expansive breadth,” he says.
2. Security operations center support
Security operations centers (SOCs) are a great use case for agentic AI because they serve as the frontline for detecting and responding to threats, says Naresh Persaud, principal, cyber risk services, at Deloitte.
With thousands of incidents to triage daily, SOCs are experiencing mounting alert fatigue. “Analysts can spend an average of 21 minutes or longer per ticket to remediate,” says Persaud, noting that documenting cases and collecting forensic data is a time-consuming task, while tracking vulnerabilities and user access anomalies can be a complex process. “What’s more, the volume of incidents is expected to rise as attackers increasingly employ AI to launch attacks on a broader scale.”
Persaud believes that adding agentic AI to SOCs makes sense given that agents can be trained to handle detection, utilize natural language processing (NLP) to produce case documentation, integrate with identity systems to correlate anomalous access, and perform automated remediation. “More important, agentic AI SOC analysts can allow SOCs to scale geometrically as work volume fluctuates.”
3. Automated triage and enriched of security event logs
Pascal Geenens, director of threat research for cybersecurity services firm Radware, says that automated triage, combined with enriched security event logs, form a strong AI agentic use case.
“Imagine an AI agent that autonomously collects indicators of compromise [IOCs] from multiple threat feeds, correlates them with internal telemetry, enriches the data with context from OSINT and CTI [cyber threat intelligence] repositories, and then drafts a structured alert for an analyst.” Instead of waiting for a SOC team to pivot manually across different platforms, the agent executes the pivoting automatically, flags anomalies, and prepares a recommended response playbook.
Geenens believes his suggested approach, like many agentic AI use cases presented here, addresses two major cybersecurity pain points: scale and speed. “Analysts are drowning in alerts and lack the time to connect dots across multiple sources,” he says. Agentic AI can effectively supplant repetitive, high-volume correlation tasks. More important, it closes the gap between detection and mitigation, enabling analysts to focus on validation and strategy rather than operations. “In practice, this doesn’t replace humans, but amplifies expertise while cutting through noise.”
4. Augmenting security talent
Another big problem in cybersecurity doesn’t involved technology — it’s the current talent gap, and AI agents provide that most practical answer, says Rahul Ramachandran, generative AI product management director at Palo Alto Networks.
“AI agents can act as force multiplier for your swamped security teams, automating the endless maintenance needed to keep your security posture solid and troubleshooting complex issues across your many different security tools,” he explains. “This frees up your best people to focus on critical threats instead of manual, repetitive work.”
The cybersecurity talent gap isn’t a temporary trend — it’s a persistent reality we’ll be facing for years, Ramachandran warns. “You simply can’t hire your way out of this problem,” he adds. “Using AI agents is a strategic decision to invest in your existing team, making them more productive, more effective and, ultimately, happier.”
5. Protecting brands against fraud
Fake domains have always been a headache, says Šarūnas Bružas, CEO of office equipment services provider Deskronic. “An AI agent can scan for new domain registrations that appear similar to your company, grab screenshots, perform WHOIS checks, and even draft takedown requests.”
Bružas reports that an AI agent recently helped him catch a phishing site in less than 20 minutes from launch. “That would have normally taken days, during which time customers could have lost data and money,” he says.
Another strong use case is detecting scam ads on social media. “Scammers run Facebook or Instagram ads that impersonate your brand, and an AI agent can alert you immediately so you can have them taken down before too many customers click,” he adds
Such incidents happen quickly, and a manual team can’t keep up with the volume, Bružas says. Every hour a phishing site or scam ad is up increases the risk for fraud while damaging customer trust. “With agents always scanning for fake sites and ads, it will take less time to detect scams, and the human team is then free to focus on review instead of routine monitoring,” he notes. “In the end, this will make work smoother, limit the time attackers have to strike, and keep customers safer.”
6. Help desk support
AI agents can be used to automate common and repetitive help desk tasks, such as provisioning access to applications or troubleshooting authentication issues, freeing team members to respond quickly to requests that may not be as straightforward, says Ed Dunnahoe, vice president of innovation at cybersecurity services firm GuidePoint Security.
“In the context of infrastructure, agents may also be able to speed-up the process of performing root cause analysis by parsing system logs more quickly, correlating results across data sources, and giving human engineers a major head start on their investigation,” he adds.
7. Autonomous real-time zero-trust policy enforcement
Every end user has a unique profile, reflecting specific behaviors, privileges, and risk scores, says Stephen Manley, CTO at cyber resilience platform provider Druva.
“Agents can monitor those users and, if there’s a deviation, can push changes to what that user can access, force a re-authentication, or even temporarily sandbox that user,” he says. This becomes even more important, he adds, for organizations that are striving for zero trust, “because you can have agents monitor non-human actors, such as other AI agents.”