ブログ
/
Proactive Security
/
October 23, 2025

Darktrace Announces Extended Visibility Between Confirmed Assets and Leaked Credentials from the Deep and Dark Web

This blog explores how Continuous Threat Exposure Management (CTEM) is reshaping defense strategies and how new Darktrace / Attack Surface Management capabilities, including Exploit Prediction Assessment and Deep & Dark Web Monitoring, help organizations turn CTEM from strategy into action.
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
Kelland Goodin
Product Marketing Specialist
Written by
Barend de Jonge
Director of Product
Default blog image
23
Oct 2025

Why exposure management needs to evolve beyond scans and checklists

The modern attack surface changes faster than most security programs can keep up. New assets appear, environments change, and adversaries are increasingly aided by automation and AI. Traditional approaches like periodic scans, static inventories, or annual pen tests are no longer enough. Without a formal exposure program, many businesses are flying blind, unaware of where the next threat may emerge.

This is where Continuous Threat Exposure Management (CTEM) becomes essential. Introduced by Gartner, CTEM helps organizations continuously assess, validate, and improve their exposure to real-world threats. It reframes the problem: scope your true attack surface, prioritize based on business impact and exploitability, and validate what attackers can actually do today, not once a year.

With two powerful new capabilities, Darktrace / Attack Surface Management helps organizations evolve their CTEM programs to meet the demands of today’s threat landscape. These updates make CTEM a reality, not just a strategy.

Too much data, not enough direction

Modern Attack Surface Management tools excel at discovering assets such as cloud workloads, exposed APIs, and forgotten domains. But they often fall short when it comes to prioritization. They rely on static severity scores or generic CVSS ratings, which do not reflect real-world risk or business impact.

This leaves security teams with:

  • Alert fatigue from hundreds of “critical” findings
  • Patch paralysis due to unclear prioritization
  • Blind spots around attacker intent and external targeting

CISOs need more than visibility. They need confidence in what to fix first and context to justify those decisions to stakeholders.

Evolving Attack Surface Management

Attack Surface Management (ASM) must evolve from static lists and generic severity scores to actionable intelligence that helps teams make the right decision now.

Joining the recent addition of Exploit Prediction Assessment, which debuted in late June 2025, today we’re introducing two capabilities that push ASM into that next era:

  • Exploit Prediction Assessment: Continuously validates whether top-priority exposures are actually exploitable in your environment without waiting for patch cycles or formal pen tests.  
  • Deep & Dark Web Monitoring: Extends visibility across millions of sources in the deep and dark web to detect leaked credentials linked to your confirmed domains.
  • Confidence Score: our newly developed AI classification platform will compare newly discovered assets to assets that are known to belong to your organization. The more these newly discovered assets look similar to assets that belong to your organization, the higher the score will be.

Together, these features compress the window from discovery to decision, so your team can act with precision, not panic. The result is a single solution that helps teams stay ahead of attackers without introducing new complexities.

Exploit Prediction Assessment

Traditional penetration tests are invaluable, but they’re often a snapshot of that point-in-time, are potentially disruptive, and compliance frameworks still expect them. Not to mention, when vulnerabilities are present, teams can act immediately rather than relying solely on information from CVSS scores or waiting for patch cycles.  

Unlike full pen tests which can be obtrusive and are usually done only a couple times per year, Exploit Prediction Assessment is surgical, continuous, and focused only on top issues Instead of waiting for vendor patches or the next pen‑test window. It helps confirm whether a top‑priority exposure is actually exploitable in your environment right now.  

For more information on this visit our blog: Beyond Discovery: Adding Intelligent Vulnerability Validation to Darktrace / Attack Surface Management

Deep and Dark Web Monitoring: Extending the scope

Customers have been asking for this for years, and it is finally here. Defense against the dark web. Darktrace / Attack Surface Management’s reach now spans millions of sources across the deep and dark web including forums, marketplaces, breach repositories, paste sites, and other hard‑to‑reach communities to detect leaked credentials linked to your confirmed domains.  

Monitoring is continuous, so you’re alerted as soon as evidence of compromise appears. The surface web is only a fraction of the internet, and a sizable share of risk hides beyond it. Estimates suggest the surface web represents roughly ~10% of all online content, with the rest gated or unindexed—and the TOR-accessible dark web hosts a high proportion of illicit material (a King’s College London study found ~57% of surveyed onion sites contained illicit content), underscoring why credential leakage and brand abuse often appear in places traditional monitoring doesn’t reach. Making these spaces high‑value for early warning signals when credentials or brand assets appear. Most notably, this includes your company’s reputation, assets like servers and systems, and top executives and employees at risk.

What changes for your team

Before:

  • Hundreds of findings, unclear what to start with
  • Reactive investigations triggered by incidents

After:

  • A prioritized backlog based on confidence score or exploit prediction assessment verification
  • Proactive verification of exposure with real-world risk without manual efforts

Confidence Score: Prioritize based on the use-case you care most about

What is it?

Confidence Score is a metric that expresses similarity of newly discover assets compared to the confirmed asset inventory. Several self-learning algorithms compare features of assets to be able to calculate a score.

Why it matters

Traditional Attack Surface Management tools treat all new discovery equally, making it unclear to your team how to identify the most important newly discovered assets, potentially causing you to miss a spoofing domain or shadow IT that could impact your business.

How it helps your team

We’re dividing newly discovered assets into separate insight buckets that each cover a slightly different business case.

  • Low scoring assets: to cover phishing & spoofing domains (like domain variants) that are just being registered and don't have content yet.
  • Medium scoring assets: have more similarities to your digital estate, but have better matching to HTML, brand names, keywords. Can still be phishing but probably with content.
  • High scoring assets: These look most like the rest of your confirmed digital estate, either it's phishing that needs the highest attention, or the asset belongs to your attack surface and requires asset state confirmation to enable the platform to monitor it for risks.

Smarter Exposure Management for CTEM Programs

Recent updates to Darktrace / Attack Surface Management directly advance the core phases of Continuous Threat Exposure Management (CTEM): scope, discover, prioritize, validate, and mobilize. The new Exploit Prediction Assessment helps teams validate and prioritize vulnerabilities based on real-world exploitability, while Deep & Dark Web Monitoring extends discovery into hard-to-reach areas where stolen data and credentials often surface. Together, these capabilities reduce noise, accelerate remediation, and help organizations maintain continuous visibility over their expanding attack surface.

Building on these innovations, Darktrace / Attack Surface Management empowers security teams to focus on what truly matters. By validating exploitability, it cuts through the noise of endless vulnerability lists—helping defenders concentrate on exposures that represent genuine business risk. Continuous monitoring for leaked credentials across the deep and dark web further extends visibility beyond traditional asset discovery, closing critical blind spots where attackers often operate. Crucially, these capabilities complement, not replace, existing security controls such as annual penetration tests, providing continuous, low-friction validation between formal assessments. The result is a more adaptive, resilient security posture that keeps pace with an ever-evolving threat landscape.

If you’re building or maturing a CTEM program—and want fewer open exposures, faster remediation, and better outcomes, Darktrace / Attack Surface Management’s new Exploit Prediction Assessment and Deep & Dark Web Monitoring are ready to help.

  • Want a more in-depth look at how Exploit Prediction Assessment functions? Read more here

Committed to innovation

These updates are part of the broader Darktrace release, which also included:

1. Major innovations in cloud security with the launch of the industry’s first fully automated cloud forensics solution, reinforcing Darktrace’s leadership in AI-native security.

2. Darktrace Network Endpoint eXtended Telemetry (NEXT) is revolutionizing NDR with the industry’s first mixed-telemetry agent using Self-Learning AI.

3. Improvements to our OT product, purpose built for industrial infrastructure, Darktrace / OT now brings dedicated OT dashboard, segmentation-aware risk modeling, and expanded visibility into edge assets and automation protocols.

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
Kelland Goodin
Product Marketing Specialist
Written by
Barend de Jonge
Director of Product

More in this series

No items found.

Blog

/

AI

/

September 17, 2026

The Problem of Re-defining Human Value in the Agentic Age

Default blog imageDefault blog image

Newsfeeds are constantly informing us about the rapid escalation of agentic AI systems. These systems move far beyond simple machine-based computations, and  the focused, defined and bounded assistance that most AI systems started out as.

The next evolution of AI will harness the agentic properties of orchestration, automation, and heightened value-chains in IT, taking on the burden of workflow management, not just workflow delivery.

In nearly all of these instances, promises are made such as ‘this will free up human time’ or ‘this will allow people to focus on higher-order strategy’. However that message is delivered, one thing is clear: in ceding the orchestration and management of work to increasingly sophisticated AI agents and agentic systems, human value will be elevated to a particular and specific layer: the ability to judge its outputs.  As AI takes on more tasks, humans should be able to focus on a higher level of governance; making sure the decisions that AI offers us are ethical, responsible and worthwhile.  

But there are two major problems with that approach.

This blog discusses the problem of how Agentic systems are re-shaping how we review information, where we fit in, and when we make decisions.  It also discusses the problem of how the increased flow of confident, generated information affects the way we make judgement.  This blog considers how human judgement needs to adapt, and how a behavioral defense approach – using techniques pioneered by Darktrace – can help us do that.

The challenge of knowing where human judgement belongs

As we confer more automated decision-making to agentic systems, it might look increasingly less like ‘granting permissions’, and more like ‘surrendering authority’.  

The judgement layer for AI-generated work is not a fixed boundary. We have become used to the idea of a ‘human in the loop’ (HITL) and, historically, relationships between humans and IT systems were reasonably clear and bounded.  Computer and software systems were programmed to carry out certain tasks or automated functions, and humans could control the gates and decision points where actions were undertaken. Even across highly complex computational workflows, human interaction was a controllable node within the process; we were able to configure and regulate. But in the agentic age, where that human interaction sits, and what it can influence shifts every time AI systems are granted autonomy.  

This leads us to the first problem: if humans are moving themselves (or are being moved) into the ‘judgement’ part of the value chain, exactly where and when do we exercise that judgement?  

Humans are no longer the sole shepherds of computer-based or software-controlled outputs.  We are at times at least one step further (and slower) behind the new agentic shepherds.  We might also be blind to what they are doing.  Not only might we be removed and blind to the actions of our AI shepherds, but with the challenge of unknown, unapproved AI systems operating beyond our control, humans might not even know that our work is being shepherded by an AI at all.  Simply put, with the advent of greater levels of autonomy and orchestration, humans are at risk of not even knowing where to apply our newly-extended powers of strategic judgement.

Shadow AI – the use of unapproved AI systems or processes – is a growing threat to the role of effective governance and oversight. Shadow AI isn't just the AI you can't see. Its the AI you already know about being used in an unapproved way. The ability to generate effective oversight of the AI systems you use (or that are used on your behalf) will be increasingly important to ensure that human judgement in the AI value chain is effective, and deliberately placed.

The problem of what makes good judgement

The second problem lies in how flawed human judgement can be.  Humans are historically, notoriously, and, sometimes dangerously, unreliable when it comes to exercising judgement.  Humans are prone to the worst kinds of bias, the seduction of malign influence, and the sometimes-overwhelming urge to succeed. AI has long had a known flaw of operating with sycophancy, providing outputs that tend to agree with or flatter the human user.  But as AI grows ever more effective, there is a risk of both hyper-enablement (where humans increasingly and knowingly enable AI despite potential harm), as well as the greater risk of suggestion. Both of these aspects could skew the newly-elevated input of human judgement.

Imagine a highly competent AI system that has just orchestrated and managed a dizzying array of processes and workflows.  The AI is designed to present the human decision-maker with recommendations; based on analysis, comparison and other programmed factors.  This is where the human judgement layer is enabled.  But what if that judgement is summarily diffused by an AI-based recommendation that emulates the decision, provides plausible but unattractive alternatives, then suggests (or, worse, directs) the human end-user to take a particular course of action.

The risk here is that you are given a recommendation, tailored to your preferences (which the AI has learned, or which you have divulged), and which appears to make perfect sense.  It appears to be a well-weighted recommendation, with sound arguments that tap into our inherent biases or inclinations so that a specific decision-path is followed. With the growth of agentic systems specifically designed to match user profiles (from Cowork agents to ‘digital twin’ models), the likelihood of agentic influence could badly skew human judgement or, at the least, devalue the proposition that humans are taking a higher-layer of strategic control over AI-based decisions.

If AI convincingly recommends something that may be problematic, it can be difficult to discern both accurate data, and the context required to make the right judgement.

Given the two problems described above, the job of exercising valuable human judgement in the agentic age can draw down to these two questions:

  • When should humans intervene in the agentic process?  
  • How can we make the best possible judgement calls?

What humans contribute that AI cannot

For all the flaws that make human judgement unreliable, people have the edge over even the most sophisticated and powerful AI systems when it comes to issues such as ethics and social context.  An AI system can, with startling granularity, rank the value of adopting a new business proposal: offering predictive metrics on costs, returns, market value, time-to-deliver operations, conformance with legal registers, etc.  But it can’t tell if the business proposal is ethically sound, or if the business venture will potentially affect groups outside of the analyzed proposal. It can’t tell you if the CEO has a ‘bad feeling’ about this effort.  It can’t tell you if this is the right thing to do.  

The ability to add social context, balance complex interpersonal dynamics, understand nuance, and to go beyond what seems economically reasonable is where human judgement can add value.  

Human judgement is difficult to encapsulate in metrics. And the way we train our development may need to adapt too. Rather than building up a gradual, experiential knowledge base, we should think about training the skill of judgement itself; especially for an agentic age.

How behavioral security strengthens AI governance

If this all feels like a vicious circle (‘I need AI help to make good judgements’ / ‘AI can twist what I need to judge’) it needn’t be. The key to this is having a defense-in-depth approach, with tools that can actually help.

This is precisely where behavioral security becomes important. The complex and nuanced way that humans exercise judgement is often rooted in our ability to recognize behavior that doesn't look right. We may not always be able to articulate it immediately, but we can often identify when an action, recommendation, or outcome feels inconsistent with the context around it. As AI systems take on more responsibility across the decision chain, preserving that ability to recognize meaningful deviations becomes increasingly important.

Darktrace’s / SECURE AI is designed to do exactly that. It applies behavioral security to AI ecosystems, helping organizations understand how people, AI tools, identities, and agents interact across the business. By learning the patterns of normal AI usage and surfacing activity that deviates from those patterns, it provides security teams with the context needed to investigate risk, understand unusual behavior, and make informed governance decisions. Rather than relying solely on predefined rules or assumptions, this behavioral understanding helps organizations distinguish between expected AI activity and behavior that warrants closer scrutiny.

This matters because we are already in an era of information overload. If humans are expected to elevate their value through strategic judgement, the ability to do this without being overwhelmed by data (good or bad) will be critical.  

We need the ability to discern when we're being misled by AI, and whether our judgement calls are being made on the basis of accurate, contextual information. Darktrace / SECURE AI provides that additional layer of defensive security for activity we cannot easily see. Whether it is suspected Shadow AI or skewed recommendations, the net result is a protected organization, where users can more effectively use AI to make positive judgements.

For those where that judgement is a critical skill (both individuals, as well as those working in security teams), improving our metacognition - the ability to understand information in a broader context - will supercharge the value of human judgement. When those judgements are grounded in context rather than assumptions we have better information to make sound decisions.

Conclusion

Human judgement is a skill that is honed over time and experience.  Darktrace’s / SECURE AI employs the same principles, but at machine-speed. Rather than influencing or directing, Darktrace / SECURE AI offers AI-enabled assurance; providing human-based judgement with the right context to make a balanced decision.  

What we judge can be valued by the legitimacy of its outputs. For AI, those outputs are valued on the speed and accuracy of the information provided.  Increasingly for humans, the value of our outputs will be based on the validity of our judgement, and how we justify our decisions in ways that engineer confidence.  

Humans often know more than we can express, while AI is prone to expressing more than it truly understands. Humans can bridge the context AI often fails to appreciate. When that judgement is supported by relevant, impartial AI systems, this is the future space where good AI governance will be exercised.

Discover Darktrace / SECURE AI.

[related-resource]

Continue reading
About the author
Jason Lusted
AI Governance Advisor

Blog

/

AI

/

September 4, 2026

Darktrace Advances Incident Investigation and AI-Agent Security with OpenAI Daybreak Models

Default blog imageDefault blog image

Earlier this year, Darktrace joined OpenAI’s Daybreak Defense Network to explore how their cyber capabilities can be integrated within Darktrace products and services to transform how security teams move from signal to action.

At the heart of this work is Darktrace's behavioral understanding of customer environments and identification of complex security incidents, combined with OpenAI models that can add context to help explain why an incident matters and its potential impact on the business. By bringing these capabilities into defensive workflows security teams already use, the goal is to give defenders not just greater visibility, but the context and guidance they need to act with confidence.

Since joining the program, we've been working with OpenAI to explore how these capabilities can address specific security challenges for defenders.

The problem we're solving

Attackers continue to change how they operate, including by using AI to increase the speed and scale of some techniques. Security teams are already managing a large volume of alerts, and the question isn't just what's happening, but how it could affect the organization. Even when an incident is fully investigated and correlated, technical severity alone doesn't tell a security team how much it actually matters to the business. That same challenge extends to internal AI adoption. As organizations adopt more AI systems and agents, security teams need visibility into their behavior, access and activity, along with the broader business context needed to identify and investigate potential risk.

Darktrace's Adaptive AI™ builds a detailed, organization-specific picture of what's normal for each environment, and uses that picture to investigate threats and identify complex security activity across domains. OpenAI's models can build on Darktrace's correlated, technically prioritized incidents by adding context that can help defenders understand what may be at stake.

What we're building

Our work is focused on two areas: supporting security investigation and response, and helping defenders identify risky behavior across enterprise AI systems and agents.

The first aligns Darktrace's behavioral understanding with OpenAI models to support  security investigation and prioritization. Darktrace's Adaptive AI continuously learns the unique patterns of normal behavior within each customer it protects, creating a deep, organization-specific understanding of its digital estate. When unusual activity emerges, OpenAI's models can draw on that context to help analysts investigate the incident, understand its significance and assess potential business consequences — reducing the need to manually assemble context from fragmented signals.

Second, we are exploring how these capabilities can support AI-agent and runtime security through Darktrace / SECURE AI™. OpenAI’s Daybreak models can build on the detections and visibility Darktrace / SECURE AI provides, connecting signals across a customer's environment and help defenders identify potentially risky behavior involving AI systems and agents. Activity that might appear isolated can instead be connected with related signals, helping defenders investigate the broader context and determine appropriate remediation.

Darktrace brings deep cybersecurity expertise, an evolving understanding of each customer's environment, and AI-driven identification of threats across the digital estate. Through the Daybreak Defense Network, Darktrace is exploring how OpenAI models can augment those capabilities in defensive security workflows — supporting incident investigation and response and improving visibility into AI-agent and runtime risk.

These capabilities are still in development, and we're excited to continue building on this work.

To learn more about how Darktrace continues to innovate to meet today's most pressing security challenges, register for our upcoming launch broadcast here.

Continue reading
About the author
あなたのデータ × DarktraceのAI
唯一無二のDarktrace AIで、ネットワークセキュリティを次の次元へ