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April 16, 2025

Force Multiply Your Security Team with Agentic AI: How the Industry’s Only True Cyber AI Analyst™ Saves Time and Stop Threats

See how Darktrace Cyber AI Analyst™, an agentic AI virtual analyst, cuts through alert noise, accelerates threat response, and strengthens your security team — all without adding headcount.
Written by
Ed Metcalf
Senior Director of Product Marketing, AI & Innovation Products
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Ed Metcalf
Senior Director of Product Marketing, AI & Innovation Products
Team collaborating in work spaceDefault blog image
16
Apr 2025

With 90million investigations in 2024 alone, Darktrace Cyber AI Analyst TM is transforming security operations with AI and has added up to 30 Full Time Security Analysts to almost 10,000 security teams.

In today’s high-stakes threat landscape, security teams are overwhelmed — stretched thin by burnout, alert fatigue, and a constant barrage of fast-moving attacks. As traditional tools can’t keep up, many are turning to AI to solve these challenges. But not all AI is created equal, and no single type of AI can perform all the functions necessary to effectively streamline security operations, safeguard your organization and rapidly respond to threats.

Thus, a multi-layered AI approach is critical to enhance threat detection, investigation, and response and augment security teams. By leveraging multiple AI methods, such as machine learning, deep learning, and natural language processing, security systems become more adaptive and resilient, capable of identifying and mitigating complex cyber threats in real time. This comprehensive approach ensures that no single AI method's limitations compromise the overall security posture, providing a robust defense against evolving threats.

As leaders in AI in cybersecurity, Darktrace has been utilizing a multi-layered AI approach for years, strategically combining and layering a range of AI techniques to provide better security outcomes. One key component of this is our Cyber AI Analyst – a sophisticated agentic AI system that avoids the pitfalls of generative AI. This approach ensures expeditious and scalable investigation and analysis, accurate threat detection and rapid automated response, empowering security teams to stay ahead of today's sophisticated cyber threats.

In this blog we will explore:

  • What agentic AI is and why security teams are adopting it to deliver a set of critical functions needed in cybersecurity
  • How Darktrace’s Cyber AI AnalystTM is a sophisticated agentic AI system that uses a multi-layered AI approach to achieve better security outcomes and enhance SOC analysts
  • Introduce two new innovative machine learning models that further augment Cyber AI Analyst’s investigation and evaluation capabilities

The rise of agentic AI

To combat the overwhelming volume of alerts, the shortage of security professionals, and burnout, security teams need AI that can perform complex tasks without human intervention, also known as agentic AI. The ability of these systems to act autonomously can significantly improve efficiency and effectiveness. However, many attempts to implement agentic AI rely on generative AI, which has notable drawbacks.

Broadly speaking, agentic AI refers to artificial intelligence systems that act autonomously as "agents," capable of carrying out complex tasks, making decisions, and interacting with tools or external systems with no or limited human intervention. Unlike traditional AI models that perform predefined tasks, it uses advanced techniques to mimic human decision-making processes, dynamically adapting to new challenges and responding to varied inputs. In a narrower definition, agentic AI often uses generative large language models (LLMs) as its core, using this to plan tasks and interactions with other systems, iteratively feeding its output into its input to accomplish more tasks than are traditionally possible with a single prompt. When described in terms of technology rather than functionality, agentic AI would be deemed as AI using this kind of generative system.

In cybersecurity, agentic AI systems can be used to autonomously monitor traffic, identify unusual patterns or anomalies indicating potential threats, and take action to respond to these possible attacks. For example, they can handle incident response tasks such as isolating affected systems or patching vulnerabilities, and triaging alerts. This reduces the reliance on human analysts for routine tasks, allowing them to focus on high-priority incidents and strategic initiatives, thereby increasing the overall efficiency and effectiveness of the SOC.

Despite their potential, agentic AI systems with a generative AI core have notable limitations. Whether based on widely used foundation models or fully custom proprietary implementations, generative AI often struggles with poor reasoning and can produce incorrect conclusions. These models are prone to "hallucinations," where they generate false information, which can be magnified through iterative processes. Additionally, generative AI systems are particularly susceptible to inheriting biases from training data, leading to incorrect outcomes, and are vulnerable to adversarial attacks, such as prompt injection that manipulates the AI's decision-making process.

Thus, choosing the right agentic AI system is crucial for security teams to ensure accurate threat detection, streamline investigations, and minimize false positives. It's essential to look beyond generative AI-based systems, which can lead to false positives and missed threats, and adopt AI that integrates multiple techniques. By considering AI systems that leverage a variety of advanced methods, organizations can build a more robust and comprehensive security strategy.  

Industry’s most experienced agentic AI analyst

First introduced in 2019, Darktrace Cyber AI AnalystTM emerged as a groundbreaking, patented solution in the cybersecurity landscape. As the most experienced AI Analyst deployed to almost 10,000 customers worldwide, Cyber AI Analyst is a sophisticated example of agentic AI, aligning closely with our broad definition. Unlike generative AI-based systems, it uses a multi-layered AI approach - strategically combining and layering various AI techniques, both in parallel and sequentially – to autonomously investigate and triage alerts with speed and precision that outpaces human teams. By utilizing a diverse set of AI methods, including unsupervised machine learning, models trained on expert cyber analysts, and custom security-specific large language models, Cyber AI Analyst mirrors human investigative processes by questioning data, testing hypotheses, and reaching conclusions at machine speed and scale. It integrates data from various sources – including network, cloud, email, OT and even third-party alerts – to identify threats and execute appropriate responses without human input, ensuring accurate and reliable decision-making.

With its ability to learn and adapt using Darktrace's unique understanding of an organization’s environment, Cyber AI Analyst highlights anomalies and passes only the most relevant activity to human users. Every investigation is thoroughly explained with natural language summaries, providing transparent and interpretable AI insights. Unlike generative AI-based agentic systems, Cyber AI Analyst's outputs are based on a comprehensive understanding of the underlying data, avoiding inaccuracies and "hallucinations," thereby dramatically reducing risk of false positives.

90 million investigations. Zero burnout.

Building on six years of innovation since launch, Darktrace's Cyber AI Analyst continues to revolutionize security operations by automating time-consuming tasks and enabling teams to focus on strategic initiatives. In 2024 alone, the sophisticated AI system autonomously conducted 90 million investigations, its analysis and correlation during these investigations resulted in escalating just 3 million incidents for human validation and resulting in fewer than 500,000 incidents deemed critical to the security of the organization. This completely changed the security operations process, providing customers with an ability to investigate every relevant alert as an unprecedented alternative to detection engineering that avoids massive quantities of risk from the traditional approach.  Cyber AI Analyst performed the equivalent of 42 million hours of human investigation for relevant security alerts.

The benefits of Cyber AI Analyst will transform security operations as we know it today:

  • Autonomously investigates thousands of alerts, distilling them into a few critical incidents — saving security teams thousands of hours and removing risk from current “triage few” processes. [See how the State of Oklahoma gained 2,561 hours of investigation time and eliminated 3,142 alerts in 3 months]
  • It decreases critical incident discoverability from hours to minutes, enabling security teams to respond faster to potential threats that will severely impact their organization. Learn how South Coast Water District went from hours to minutes in incident discovery.
  • It reduces false positives by 90%, giving security teams confidence in its accuracy and output.
  • Delivers the output of up to 30 full-time analysts – without the cost, burnout, or ramp-up time, while elevating existing human security analysts to validation and response

Cyber AI Analyst allows security teams to allocate their resources more effectively, focusing on genuine threats rather than sifting through noise. This not only enhances productivity but also ensures that critical alerts are addressed promptly, minimizing potential damage and improving overall cyber resilience.

Always innovating - Next-generation AI models for cybersecurity

As empowering defenders with AI has never been more critical, Darktrace remains committed to driving innovation that helps our customers proactively reduce risk, strengthen their security posture, and uplift their teams. To further enhance security teams, Darktrace is introducing two next-generation AI models for cybersecurity within Cyber AI Analyst, including:

  • Darktrace Incident Graph Evaluation for Security Threats (DIGEST): Using graph neural networks, this model analyzes how attacks progress to predict which threats are likely to escalate — giving your team earlier warnings and sharper prioritization.  This means earlier warnings, better prioritization, and fewer surprises during active threats.
  • Darktrace Embedding Model for Investigation of Security Threats - Version 2 (DEMIST-2): This new language model is purpose-built for cybersecurity. With deep contextual understanding, it automates critical human-like analysis— like assessing hostnames, file sensitivity, and tracking users across environments. Unlike large general-purpose models, it delivers superior performance with a smaller footprint. Working across all our deployment types, including on-prem and cloud, it can run without internet access, keeping inference local.

Unlike the foundational LLMs that power many generative and agentic systems, these models are purpose-built for cybersecurity, supported by insights of over 200 security analysts and is capable of mimicking how an analyst thinks, to bring AI-based precision and depth of analysis into the SOC. By understanding how attacks evolve and predicting which threats are most likely to escalate, these machine learning models enable Cyber AI AnalystTM to provide earlier detection, sharper prioritization, and faster, more confident decision-making.

Conclusion

Darktrace Cyber AI AnalystTM redefines security operations with proven agentic AI — delivering autonomous investigations and faster response times, while significantly reducing false positives. With powerful new models like DIGEST and DEMIST-2, it empowers security teams to prioritize what matters, cut through noise, and stay ahead of evolving threats — all without additional headcount. As cyber risk grows, Cyber AI Analyst stands out as a force multiplier, driving efficiency, resilience, and confidence in every SOC.

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Additional resources

Learn more about Cyber AI Analyst

Explore the solution brief, learn how Cyber AI Analyst combines advanced AI techniques to deliver faster, more effective security outcomes

Written by
Ed Metcalf
Senior Director of Product Marketing, AI & Innovation Products
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Ed Metcalf
Senior Director of Product Marketing, AI & Innovation Products

When Open Source Is Weaponized: Analysis of a Trojanized 7 Zip Installer

Network
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May 19, 2026
Justin Torres
Cyber Analyst
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June 3, 2026

Stopping Stealth Attacks with Precision: How Núclea Prevented a Breach Without Disruption

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Núclea is a Brazilian data and technology company that supports the country’s financial system by delivering digital services exclusively to banks and financial institutions. Operating in an environment where trust, availability, and data integrity are critical, the company faces a threat landscape that has evolved rapidly—particularly with the rise of AI-driven cyberattacks.

Brazil has experienced a wave of successful cyber incidents targeting financial institutions, many of them enabled by insiders or compromised credentials. The result was a noticeable shift in attacker strategy: instead of focusing on end customers, threat actors began targeting the institutions and platforms that underpin the financial ecosystem itself.

“Attacks became far more directed and contextual,” explains Guilherme, who leads incident response within Núclea’s security platform engineering team. “They weren’t noisy or obviously malicious—they were precise, patient, and designed to blend into normal operations.”

That precision was on full display in January 2026, when Núclea faced one of the most convincing phishing attacks the team had seen.

A real attack, built on trust and context

The attack began with a seemingly routine email.

It was sent from a real Brazilian government institution, using legitimate infrastructure and valid credentials that were later confirmed to have been compromised. Núclea had an established, ongoing relationship with this organization, and the email’s language, tone, and subject matter aligned perfectly with the type of communication the recipient team handled every day.

Attached to the email was a PDF document containing content that looked entirely legitimate.

The problem? A single URL embedded inside that PDF.

“The message itself was correct. The sender was real. The context was familiar. Even the document content made sense,” Guilherme explains. “There was just one small element that didn’t belong.”

That small detail was enough to initiate a full attack chain.

What the attackers were trying to do

If clicked, the URL would have downloaded a malicious payload designed to:

  • Collect information about the user and device
  • Identify where the system was located within the financial ecosystem
  • Install remote access tools to maintain control
  • Deploy an infostealer to extract sensitive data
  • Execute anti-forensic scripts to erase traces of the intrusion

In other words, it was a carefully engineered operation designed for persistence and stealth, not immediate disruption.

The attack also employed urgency—a classic social engineering technique. When the link didn’t open as expected, employees requested assistance from the security team, insisting the document was important and needed to be accessed quickly.

This is precisely the kind of scenario where traditional security tools struggle: almost everything about the interaction is legitimate.

Where Darktrace made the difference

Instead of blocking the entire message or relying on known indicators of compromise, Darktrace focused on behavioral context.

Darktrace recognized:

  • That the sending organization was normally trusted
  • That the communication pattern matched historical behavior
  • That the PDF content itself was not suspicious

But it also identified that the URL embedded within the document deviated from established behavioral patterns.

Rather than disrupting business operations, Darktrace took precise action: it rewrote the URL, preventing the malicious download while leaving the rest of the email untouched.

“When we analyzed it afterward, it became clear how dangerous the attack would have been,” says Guilherme. “But it never progressed—because Darktrace acted at exactly the right point.”

Subsequent forensic analysis confirmed the payload’s malicious intent. The attack never succeeded.

Precision over disruption

For Núclea, this incident reinforced a critical lesson: modern attacks don’t always look malicious—they hide within normal activity.

“What stands out to me is the precision,” Guilherme says. “Darktrace doesn’t rely on big, obvious signals. It’s effective in situations that fall outside the standard patterns we all know.”

Building resilience in a high trust ecosystem

For Núclea, cybersecurity is not just a defensive measure—it’s a business enabler.

Availability failures or successful breaches in the financial ecosystem can have immediate, large-scale consequences, from financial loss to reputational damage. Preventing those outcomes protects not just Núclea, but its partners and customers as well.

“Cyber resilience means keeping the business running—even under attack,” Guilherme explains. “And that requires people, processes, and technology working together.”

As AI continues to accelerate both attacks and defenses, the role of security is evolving. Precision, behavioral understanding, and intelligent automation are no longer optional—they’re essential.

“The easy days were yesterday,” Guilherme says. “The challenges ahead are bigger. We need to be prepared—internally and with partners that help us build resilience.”

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June 1, 2026

Defend What You Trust: Stories from the Front Lines of Modern Cyber Defense

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Modern attacks don’t always announce themselves, follow obvious patterns, or rely on known malware. Often, they move quietly inside trusted systems, authenticated sessions, and everyday behavior.

They don’t break in. They blend in.

That’s why an AI-powered defense is essential. It turns invisible signals into actionable insights at a scale neither analysts nor traditional tools can achieve alone.

Confidence is creating risk

One of the most dangerous assumptions in cybersecurity today is that strong controls equal strong protection.

Multi-factor authentication (MFA), for example, is widely viewed as a foundational safeguard. But as the CISO for a professional sports organization explains, that confidence can be misplaced. “A lot of organizations assume that once you have MFA, those accounts are safe. That’s not true.”

In one instance, his team identified a sophisticated attack where a threat actor bypassed MFA entirely, not by breaking it, but by going around it. A user’s authenticated session was hijacked and re-used, allowing the attacker to impersonate them without triggering traditional controls.

“Darktrace picked up that a session had been re-injected by the hacker, and we were able to block it right away,” he explains.

Attackers anticipate what we miss

Even well-trained users can become entry points.

“An email bypassed our existing security tools,” shares the VP of IT at a U.S.-based risk management services provider.  “The user missed one signal and entered their credentials into a malicious site. That’s what the bad guys count on.”

The organization responded quickly, but not before damage was done. Crucially, this occurred while Darktrace was in “watch mode,” before autonomous response was fully enabled. “Darktrace would have seen that and shut it down immediately,” he notes.

Mistakes and oversights like misconfigurations, forgotten machines, and missed patches can create serious vulnerabilities.

The CIO of a utility services organization shares an instance when Darktrace detected a breach to a client’s network via their ZTNA VPN due to misconfigured MFA. “Darktrace alerted us and autonomously blocked the scanning, preventing what could have been a ransomware-type incident.”  

The most dangerous threats are already inside

The Head of Security at a global business services provider knows firsthand how blind spots can persist inside environments. His team uncovered evidence of dormant ransomware artifacts sitting unnoticed within a company’s environment ¬¬– long before modern detection was in place.

“During a routine file transfer, Darktrace flagged the suspicious activity, identified the ransomware, and immediately quarantined the server,” he recalls.  While the attack was never executed, the implication was significant: the risk existed long before it was finally detected.

Cyber threats are also successful because they take advantage of normal human behavior, exploiting moments of cognitive overload, urgency, and trust.

The Executive Director of IT and Business Applications at a pharmaceutical lab describes the time Darktrace flagged an employee logging into Microsoft 365 from Singapore, despite him being physically located in the U.S. Darktrace immediately cut off his access and within minutes revealed that the employee’s son was using a VPN to play a video game.

While the threat was benign, it demonstrated the strength of AI to use contextual information to detect threats other tools miss. The information also saved security analysts hours of investigation and minimized downtime for the employee. “That level of precision and speed isn’t just convenient, it’s game changing.”

“Unusual” behavior is the new red flag

Detecting modern threats requires an understanding of what “normal” looks like and recognizing when something subtly deviates.

One security leader  at an AI technology enterprise described a scenario in which an employee connected to a proxy service in China. The service itself was legitimate, and although traditional tools didn’t flag it, the behavior was unusual for that user specifically.

“That’s what Darktrace picked up on. The activity turned out to be benign, but without visibility into behavioral deviations, it could just as easily have been something more serious.”

AI shifts defense from reaction to anticipation

These stories point to a fundamental shift by cyber attackers, both tactically and strategically. Because traditional security tools were built to detect what’s already known, modern attacks are often:

  • Credential-based, not malware-based
  • Behavioral, not signature-based
  • Subtle, not overt

They may operate within the boundaries of what appears normal, exploiting what organizations trust, not what they block:

  • Trusted sessions
  • Legitimate services
  • Human error

This is where AI is changing the equation. Rather than relying on predefined rules or known threat signatures, AI can:

  • Establish a baseline of normal behavior
  • Detect subtle anomalies in real time
  • Act autonomously to contain potential threats

Resilience, not perfection, is the new security standard

As these frontline experiences show, the organizations that lead are those that move beyond reactive defense and embrace AI as a core part of their strategy.

It eliminates the blind spots and uncertainty, says the CISO of a professional sports organization. “If you lack visibility, you’re not managing risk, you’re assuming it. AI gives you the actionable insights needed to turn uncertainty into control.”

And it provides the speed and agility that are vital when seconds matter, says the Executive Director of IT and Business Applications. “When Darktrace alerted us at 3:00 am to a ransomware attack, it had already quarantined the affected systems, blocked the attacker’s access, and provided us with the critical details and time needed to investigate. That action likely saved us hundreds of thousands, if not millions, of dollars.”

The modern SOC has become a cornerstone of enterprise resilience, responsible for protecting data and operational continuity while enabling digital growth and innovation. For today’s security professional, that means success is no longer measured by what they keep out, but by what they protect: revenue, reputation, and trust.

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