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November 1, 2023

The Case for Self-Learning AI in Cloud Security

AI that learns each unique cloud environment gives real-time visibility, contextual threat detection, and truly autonomous, cloud-native response — overcoming blind spots and limits of agentless or agent-based tools.
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
Nabil Zoldjalali
VP, Field CISO
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01
Nov 2023

Widespread use of the cloud continues to transform business, while cyber security solutions race to keep up. Today’s multi-cloud environments introduce complexity and gaps in visibility that open doors for attackers. Given the dynamic nature of the cloud, these blind spots are constantly changing. And given its scalability, simple mistakes like a minor misconfiguration can lead to disproportionately large security incidents.

Enterprises can no longer afford to rely on disparate tools and static, point-in-time views of risk. The cloud is inherently complex, and security tools shouldn’t aim to simplify that complexity, but instead harness it, using its scale and intricacy to its advantage.

In a world where the cloud is highly customizable and every cloud is different, a one-size-fits-all approach to cloud security fails to adapt to the nuances of an individual environment. This blog explores how harnessing AI that learns and understands the unique organization can give security teams the visibility, understanding, and real-time detection and response needed to secure the cloud.

Security hinges on action

Typically, cloud security tends to fall into one of two camps:

  • Agentless approaches used by most Cloud Security Posture Management (CSPM) vendors that promise quick and easy installation with minimal disruption of operations, and
  • Agent-based approaches that offer finer granularity but may mean a lengthy, time-consuming, and expensive set-up process.

Both approaches have inherent drawbacks. Agentless solutions typically don’t give security teams the real-time awareness needed to detect emerging threats – be that a malicious insider, a zero-day exploit, or something else. On the other hand, agent-based solutions provide limited reach and scalability, usually being deployed in an area of the cloud the security team already knew posed a risk, offering no new insight and leaving blind spots untouched.

So cloud security today seems to be stuck in a dilemma. And another issue for both methods is that these products may be able to alert analysts when something goes wrong, but lack the ability to mount a genuine response. Even newer solutions claiming to provide automated response are usually referring to automating the process of sending alerts and opening tickets.

Rapid response is the holy grail

The same attributes that make the cloud so useful and attractive to organizations – speed, agility, availability, and scale – hold a symmetrical appeal for attackers. When cyber-attacks in the cloud unfold rapidly, it’s not enough to simply open a ticket and wait for somebody on the other end to pick it up. (If anything, having to field too many tickets can actually bog down triage and investigation, and delay rather than hasten response.) The ultimate test for useful response comes down to whether or not the security team is willing to use it. Response capabilities that never get turned on, with security teams fearful of disruption, miss the point entirely.

Effective response requires an understanding of when and how to respond, as well as having the cloud-native mechanisms to carry out the action. We can break this down into three steps:

Step 1: Beyond Visibility: Real-Time Understanding

Today’s static cloud security solutions provide snapshots of your environment prior to integration and installation. Static insights help validate and set up controls before deployment, but the real risks related to cloud migration appear later.

To drive the right response, your security solution must deliver a real-time, holistic view of your organization’s cloud environment, not just a generic sense of what the environment looks like.

Understanding risk related to the cloud requires more than just visibility. It requires understanding the various patterns of behavior across the environment, and knowing the nuances in how applications and workloads are architected. Who has access to what? Which virtual machines typically connect with each other? Is this container behaving as expected? Is this new Lambda function expected?

Darktrace / CLOUD uses Self-Learning AI to see and understand your unique organization at the cloud network, architectural, and management layers. The ability of AI to recognize patterns across vast quantities of data puts it in a unique position to give security teams genuine insight into what’s happening in their cloud environment right now.

Each deployment and specific use of AI is different (based on your unique environment) but always includes an architectural view of your cloud footprint that aligns security and DevOps teams throughout the deployment lifecycle.  

One beta customer reported deploying Darktrace/Cloud was:

like flipping on a light switch in a dark room."

Step 2: Detection must apply context

With a true understanding of exactly what’s ‘normal’ in your cloud – which users are connecting to what resources, who has access to specific workloads, groups, overlaps, and privileges — the solution progresses toward response by teaching itself to spot what isn’t so normal.

A static snapshot of your cloud security posture can surface unpatched vulnerabilities and problematic misconfigurations, but the insight ends there. Cloud security solutions based on static views and point-in-time visibility can’t connect the dots to deliver the end-goal: the ability to spot real-time threats.

Darktrace/Cloud delivers meaningful insight into vulnerabilities and misconfigurations, but its real-time understanding also enables detection of emerging threats. And combining with other Darktrace modules like Darktrace / NETWORK and Darktrace/Email, it enriches these findings with business context to find and shut down emerging threats in seconds. This business-wide context to understand your cloud footprint and how it interacts with your on-premises infrastructure, endpoints, and applications

Step 3: Response must be truly autonomous

By understanding your unique cloud footprint within the context of your own business, Darktrace/Cloud uniquely detects when something unusual is occurring that requires a response right now.

The use of AI to understand your environment enables a truly autonomous and precise cloud-native response. The platform can take targeted action to stop only the threatening behaviors as they appear, without disrupting regular business operations.

Because the platform understands your complete cloud architecture, it also knows what cloud-native mechanisms are at its disposal to initiate a real response. Automated real-time responses include cloud-native actions like detaching EC2 instances and applying security groups to contain risky assets.

See it in action

Darktrace is offering 30-day free trials of Darktrace/Cloud that combine easy install with unprecedented understanding of multi-cloud environments. Click here to register your interest and experience the benefits first-hand.

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
Nabil Zoldjalali
VP, Field CISO

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Proactive Security

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January 7, 2026

How a leading bank is prioritizing risk management to power a resilient future

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As one of the region’s most established financial institutions, this bank sits at the heart of its community’s economic life – powering everything from daily transactions to business growth and long-term wealth planning. Its blend of physical branches and advanced digital services gives customers the convenience they expect and the personal trust they rely on. But as the financial world becomes more interconnected and adversaries more sophisticated, safeguarding that trust requires more than traditional cybersecurity. It demands a resilient, forward-leaning approach that keeps pace with rising threats and tightening regulatory standards.

A complex risk landscape demands a new approach

The bank faced a challenge familiar across the financial sector: too many tools, not enough clarity. Vulnerability scans, pen tests, and risk reports all produced data, yet none worked together to show how exposures connected across systems or what they meant for day-to-day operations. Without a central platform to link and contextualize this data, teams struggled to see how individual findings translated into real exposure across the business.

  • Fragmented risk assessments: Cyber and operational risks were evaluated in silos, often duplicated across teams, and lacked the context needed to prioritize what truly mattered.
  • Limited executive visibility: Leadership struggled to gain a complete, real-time view of trends or progress, making risk ownership difficult to enforce.
  • Emerging compliance pressure: This gap also posed compliance challenges under the EU’s Digital Operational Resilience Act (DORA), which requires financial institutions to demonstrate continuous oversight, effective reporting, and the ability to withstand and recover from cyber and IT disruptions.
“The issue wasn’t the lack of data,” recalls the bank’s Chief Technology Officer. “The challenge was transforming that data into a unified, contextualized picture we could act on quickly and decisively.”

As the bank advanced its digital capabilities and embraced cloud services, its risk environment became more intricate. New pathways for exploitation emerged, human factors grew harder to quantify, and manual processes hindered timely decision-making. To maintain resilience, the security team sought a proactive, AI-powered platform that could consolidate exposures, deliver continuous insight, and ensure high-value risks were addressed before they escalated.

Choosing Darktrace to unlock proactive cyber resilience

To reclaim control over its fragmented risk landscape, the bank selected Darktrace / Proactive Exposure Management™ for cyber risk insight. The solution’s ability to consolidate scanner outputs, pen test results, CVE data, and operational context into one AI-powered view made it the clear choice. Darktrace delivered comprehensive visibility the team had long been missing.

By shifting from a reactive model to proactive security, the bank aimed to:

  • Improve resilience and compliance with DORA
  • Prioritize remediation efforts with greater accuracy
  • Eliminate duplicated work across teams
  • Provide leadership with a complete view of risk, updated continuously
  • Reduce the overall likelihood of attack or disruption

The CTO explains: “We needed a solution that didn’t just list vulnerabilities but showed us what mattered most for our business – how risks connected, how they could be exploited, and what actions would create the biggest reduction in exposure. Darktrace gave us that clarity.”

Targeting the risks that matter most

Darktrace / Proactive Exposure Management offered the bank a new level of visibility and control by continuously analyzing misconfigurations, critical attack paths, human communication patterns, and high-value assets. Its AI-driven risk scoring allowed the team to understand which vulnerabilities had meaningful business impact, not just which were technically severe.

Unifying exposure across architectures

Darktrace aggregates and contextualizes data from across the bank’s security stack, eliminating the need to manually compile or correlate findings. What once required hours of cross-team coordination now appears in a single, continuously updated dashboard.

Revealing an adversarial view of risk

The solution maps multi-stage, complex attack paths across network, cloud, identity systems, email environments, and endpoints – highlighting risks that traditional CVE lists overlook.

Identifying misconfigurations and controlling gaps

Using Self-Learning AI, Darktrace / Proactive Exposure Management spots misconfigurations and prioritizes them based on MITRE adversary techniques, business context, and the bank’s unique digital environment.

Enhancing red-team and pen test effectiveness

By directing testers to the highest-value targets, Darktrace removes guesswork and validates whether defenses hold up against realistic adversarial behavior.

Supporting DORA compliance

From continuous monitoring to executive-ready reporting, the solution provides the transparency and accountability the bank needs to demonstrate operational resilience frameworks.

Proactive security delivers tangible outcomes

Since deploying Darktrace / Proactive Exposure Management, the bank has significantly strengthened its cybersecurity posture while improving operational efficiency.

Greater insight, smarter prioritization, stronger defensee

Security teams are now saving more than four hours per week previously spent aggregating and analyzing risk data. With a unified view of their exposure, they can focus directly on remediation instead of manually correlating multiple reports.

Because risks are now prioritized based on business impact and real-time operational context, they no longer waste time on low-value tasks. Instead, critical issues are identified and resolved sooner, reducing potential windows for exploitation and strengthening the bank’s ongoing resilience against both known and emerging threats.

“Our goal was to move from reactive to proactive security,” the CTO says. “Darktrace didn’t just help us achieve that, it accelerated our roadmap. We now understand our environment with a level of clarity we simply didn’t have before.”

Leadership clarity and stronger governance

Executives and board stakeholders now receive clear, organization-wide visibility into the bank’s risk posture, supported by consistent reporting that highlights trends, progress, and areas requiring attention. This transparency has strengthened confidence in the bank’s cyber resilience and enabled leadership to take true ownership of risk across the institution.

Beyond improved visibility, the bank has also deepened its overall governance maturity. Continuous monitoring and structured oversight allow leaders to make faster, more informed decisions that strategically align security efforts with business priorities. With a more predictable understanding of exposure and risk movement over time, the organization can maintain operational continuity, demonstrate accountability, and adapt more effectively as regulatory expectations evolve.

Trading stress for control

With Darktrace, leaders now have the clarity and confidence they need to report to executives and regulators with accuracy. The ability to see organization-wide risk in context provides assurance that the right issues are being addressed at the right time. That clarity is also empowering security analysts who no longer shoulder the anxiety of wondering which risks matter most or whether something critical has slipped through the cracks. Instead, they’re working with focus and intention, redirecting hours of manual effort into strategic initiatives that strengthen the bank’s overall resilience.

Prioritizing risk to power a resilient future

For this leading financial institution, Darktrace / Proactive Exposure Management has become the foundation for a more unified, data-driven, and resilient cybersecurity program. With clearer, business-relevant priorities, stronger oversight, and measurable efficiency gains, the bank has strengthened its resilience and met demanding regulatory expectations without adding operational strain.

Most importantly, it shifted the bank’s security posture from a reactive stance to a proactive, continuous program. Giving teams the confidence and intelligence to anticipate threats and safeguard the people and services that depend on them.

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About the author
Kelland Goodin
Product Marketing Specialist

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AI

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December 22, 2025

The Year Ahead: AI Cybersecurity Trends to Watch in 2026

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Introduction: 2026 cyber trends

Each year, we ask some of our experts to step back from the day-to-day pace of incidents, vulnerabilities, and headlines to reflect on the forces reshaping the threat landscape. The goal is simple:  to identify and share the trends we believe will matter most in the year ahead, based on the real-world challenges our customers are facing, the technology and issues our R&D teams are exploring, and our observations of how both attackers and defenders are adapting.  

In 2025, we saw generative AI and early agentic systems moving from limited pilots into more widespread adoption across enterprises. Generative AI tools became embedded in SaaS products and enterprise workflows we rely on every day, AI agents gained more access to data and systems, and we saw glimpses of how threat actors can manipulate commercial AI models for attacks. At the same time, expanding cloud and SaaS ecosystems and the increasing use of automation continued to stretch traditional security assumptions.

Looking ahead to 2026, we’re already seeing the security of AI models, agents, and the identities that power them becoming a key point of tension – and opportunity -- for both attackers and defenders. Long-standing challenges and risks such as identity, trust, data integrity, and human decision-making will not disappear, but AI and automation will increase the speed and scale of the cyber risk.  

Here's what a few of our experts believe are the trends that will shape this next phase of cybersecurity, and the realities organizations should prepare for.  

Agentic AI is the next big insider risk

In 2026, organizations may experience their first large-scale security incidents driven by agentic AI behaving in unintended ways—not necessarily due to malicious intent, but because of how easily agents can be influenced. AI agents are designed to be helpful, lack judgment, and operate without understanding context or consequence. This makes them highly efficient—and highly pliable. Unlike human insiders, agentic systems do not need to be socially engineered, coerced, or bribed. They only need to be prompted creatively, misinterpret legitimate prompts, or be vulnerable to indirect prompt injection. Without strong controls around access, scope, and behavior, agents may over-share data, misroute communications, or take actions that introduce real business risk. Securing AI adoption will increasingly depend on treating agents as first-class identities—monitored, constrained, and evaluated based on behavior, not intent.

-- Nicole Carignan, SVP of Security & AI Strategy

Prompt Injection moves from theory to front-page breach

We’ll see the first major story of an indirect prompt injection attack against companies adopting AI either through an accessible chatbot or an agentic system ingesting a hidden prompt. In practice, this may result in unauthorized data exposure or unintended malicious behavior by AI systems, such as over-sharing information, misrouting communications, or acting outside their intended scope. Recent attention on this risk—particularly in the context of AI-powered browsers and additional safety layers being introduced to guide agent behavior—highlights a growing industry awareness of the challenge.  

-- Collin Chapleau, Senior Director of Security & AI Strategy

Humans are even more outpaced, but not broken

When it comes to cyber, people aren’t failing; the system is moving faster than they can. Attackers exploit the gap between human judgment and machine-speed operations. The rise of deepfakes and emotion-driven scams that we’ve seen in the last few years reduce our ability to spot the familiar human cues we’ve been taught to look out for. Fraud now spans social platforms, encrypted chat, and instant payments in minutes. Expecting humans to be the last line of defense is unrealistic.

Defense must assume human fallibility and design accordingly. Automated provenance checks, cryptographic signatures, and dual-channel verification should precede human judgment. Training still matters, but it cannot close the gap alone. In the year ahead, we need to see more of a focus on partnership: systems that absorb risk so humans make decisions in context, not under pressure.

-- Margaret Cunningham, VP of Security & AI Strategy

AI removes the attacker bottleneck—smaller organizations feel the impact

One factor that is currently preventing more companies from breaches is a bottleneck on the attacker side: there’s not enough human hacker capital. The number of human hands on a keyboard is a rate-determining factor in the threat landscape. Further advancements of AI and automation will continue to open that bottleneck. We are already seeing that. The ostrich approach of hoping that one’s own company is too obscure to be noticed by attackers will no longer work as attacker capacity increases.  

-- Max Heinemeyer, Global Field CISO

SaaS platforms become the preferred supply chain target

Attackers have learned a simple lesson: compromising SaaS platforms can have big payouts. As a result, we’ll see more targeting of commercial off-the-shelf SaaS providers, which are often highly trusted and deeply integrated into business environments. Some of these attacks may involve software with unfamiliar brand names, but their downstream impact will be significant. In 2026, expect more breaches where attackers leverage valid credentials, APIs, or misconfigurations to bypass traditional defenses entirely.

-- Nathaniel Jones, VP of Security & AI Strategy

Increased commercialization of generative AI and AI assistants in cyber attacks

One trend we’re watching closely for 2026 is the commercialization of AI-assisted cybercrime. For example, cybercrime prompt playbooks sold on the dark web—essentially copy-and-paste frameworks that show attackers how to misuse or jailbreak AI models. It’s an evolution of what we saw in 2025, where AI lowered the barrier to entry. In 2026, those techniques become productized, scalable, and much easier to reuse.  

-- Toby Lewis, Global Head of Threat Analysis

Conclusion

Taken together, these trends underscore that the core challenges of cybersecurity are not changing dramatically -- identity, trust, data, and human decision-making still sit at the core of most incidents. What is changing quickly is the environment in which these challenges play out. AI and automation are accelerating everything: how quickly attackers can scale, how widely risk is distributed, and how easily unintended behavior can create real impact. And as technology like cloud services and SaaS platforms become even more deeply integrated into businesses, the potential attack surface continues to expand.  

Predictions are not guarantees. But the patterns emerging today suggest that 2026 will be a year where securing AI becomes inseparable from securing the business itself. The organizations that prepare now—by understanding how AI is used, how it behaves, and how it can be misused—will be best positioned to adopt these technologies with confidence in the year ahead.

Learn more about how to secure AI adoption in the enterprise without compromise by registering to join our live launch webinar on February 3, 2026.  

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