Cybersecurity at the Speed ​​of AI: Agent AI Supercharging SOC Teams

Security operations centers (SOCs) are under siege from a new wave of automated enemy attacks. These attacks move at an unprecedented speed and are difficult to detect, track and defend against.

With enemies performing break times of just two minutes and seven secondsit's not a question of if SOC will be attacked, it's when. And 77% Enterprises have already suffered from enemy AI attacks.

For a SOC to protect itself and its company's infrastructure, speed is critical.

Enter an AI agent

Agent AI helps SOCs automate decisions, adapt to changing threats, and streamline workflows, including alert triage and incident response. It has been proven effective in improving efficiency and strengthening security by identifying threats while reducing the manual effort required to detect them.

Major cybersecurity providers offering AI agent solutions for SOCs include Arcanna.ai, Cato Networks, Cisco Security Cloud, CrowdStrike (Falcon platform by Charlotte AI), Dropzone AI, Google Cloud Security AI Workbench, Microsoft Security Copilot , Nagomi Security, Palo Alto Networks and Scalar.

“The speed of cyber attacks today requires security teams to rapidly analyze large volumes of data to detect, investigate and respond more quickly. Enemies are setting records, with break times of just over two minutes, leaving no room for delay,” said George Kurtz, president, CEO and co-founder People's Striketo VentureBeat in a recent interview.

Plan for SOC and agent AI teams to strengthen each other

For agent AI or broader SOC AI implementations to be successful, central human workflows are critical. A recent Gartner report, “Prediction 2025: There will never be an Autonomous SOC,” reinforces VentureBeat's perspective on how SOCs are piloting and adopting agent AI and broader AI apps and platforms. “Security leaders and senior operations staff need to identify where human-driven SOC functions are lagging and how to move SOC analysts into roles that require more human-in-the-loop decision-making,” said Gartner.

The report predicts that by 2026, AI will increase SOC efficiency by 40% compared to 2024 efficiency, beginning a shift in SOC knowledge towards AI development, maintenance and protection.

To effectively integrate agent AI, SOCs need a clear framework that balances technology with human experience. Gartner's extended SOC model below shows how roles, capabilities and goals align to increase efficiency and agility.

SOC challenges are a perfect use case for agent AI

SOCs need agent AI that matches the speed and vision of attackers if they are to stand a chance of thwarting an intrusion or breach attempt.

Many SOCs are understaffed. Making sense of data from legacy security information and incident management (SIEM) systems that lack visualization techniques or the ability to use graph databases to map threats is also a challenge for many.

The need to get away from thinking in lists, and think more in graphs as attackers do when planning a breach, is one of several factors. driving a strong graph database arms race throughout the industry.

Struggling to keep up with the number of alerts, false positives and ongoing maintenance work, SOC teams face these challenges every day:

Legacy systems leave SOCs open to growing AI threats. SOCs are still burdened with outdated SIEM systems, legacy endpoint detection and response (EDR), firewalls, and intrusion detection systems (IDS/IPS) that are ill-equipped to deal with the speed and complexity of cyber-driven threats. AI. Shlomo Kramer, CEO Cato Networksto VentureBeat during a recent interview“The biggest threat to organizations is the complexity of their security infrastructure. Point products create gaps in their security posture, leaving them prime targets for threat actors.” Kramer said, “Over the next five years, I see cyber threats evolving over on three dimensions: tactically, with AI-versus-AI battles; and strategically, shaped by geopolitical conflicts it's up to distributed legacy devices to defend against these growing threats.”

Chronic alert fatigue leads to wasted motivational efforts and high employee turnover. SOC analysts struggle to keep up with thousands of alerts, false alerts and inconsistent reports from multiple legacy SIEM and SOAR systems across their installations. CISOs report that they see up to 10,000 incidents per day encountered on the broad base of their operations center systems. They question whether the best use of the analysts' time is to find the three or four that are the real threats when AI has proven to be able to detect anomalous events.

Organizations are understaffed for key SOC functions. Many entrepreneurs find it nearly impossible to scale their SOC teams with internal talent alone. While outsourcing is always an option, SOC teams must invest in ongoing training and career development of their team to maintain business knowledge while strengthening cyber expertise.

A rising tidal wave of security data threats threatens to overwhelm SOC teams. Kurtz highlighted the seriousness of the challenge in a recent interview, “The problem of data is one of the main problems in security, and this is one of the reasons I started CrowdStrike. That's why I created the architecture we have, and it's very difficult for SOC teams to sort through this amount of data and volume to detect threats. “

Where agent AI makes an impact

The most significant payoff from agent AI will come from supplementing SOC analysts and teams with automation of routine tasks while giving them newer intelligence tools to learn with.

VentureBeat sees agent AI impacting the following areas:

Achieving efficiency gains at scale for the most routine, repetitive tasks. Pilot systems and production AI agents are delivering better efficiency by automating routine tasks at scale. Vasu Jakkal, corporate vice president at Microsoft, shared with VentureBeat in a recent interview the results of her company's research into the productivity benefits of Security Copilot. “The study showed that early career professionals using Security Copilot were 26% faster and 35% more accurate. Experienced professionals using the tool were 22% faster and 7% more accurate, with 90% expressing a desire to use it again,” said Sakkal.

Threat detection, analysis and intelligence in real time, while detecting anomalies in large databases. Agent AI apps and the platforms that support them are effective in identifying risks and anomalies that people might miss. And human-in-the-loop design helps keep AI agent models constantly learning and fine-tuning their ability to identify threats.

Helping SOCs accelerate incident response. Central to the design of every app, system and agent AI platform is the ability to identify and isolate key incident response tasks in real-time to remediate threats faster. VentureBeat spoke with him recently Boar CTO Eldad Livni about his company's multi-agent system, which he described as “transforming SOC operations by breaking complex workflows into specific, interconnected tasks handled by specific agents.” This approach ensures that all alerts are investigated, investigated and resolved with precision, reducing human error and allowing SOC teams to scale operations effectively. “

Continuous Learning. Agent AI enhances detection engineering in SOCs, where systems search large threat intelligence databases at scale. LLMs are trained to help security teams distinguish real threats from false ones, delivering real-time, contextual insights that save SOC analysts valuable time. VentureBeat has learned that these capabilities are driving measurable improvements in threat response.

The success of an Agentic AI is entirely dependent on human cooperation

“A place is not about human beings; it's about contributing to people,” Elia ZaitsevCTO of CrowdStrike, to VentureBeat in earlier interview. “It's that AI-assisted human, which I think is the main idea… for too long has wanted to replace the people. I think that is very misleading, especially in cyber. “