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October 23, 2025

Darktrace Redefines NDR: Industry-First Autonomous Threat Investigation from Network to Endpoint with Agentic AI

Darktrace delivers the next evolution of NDR, extending an industry-first bridge across the network and endpoint gap with Self-Learning AI.
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
Mikey Anderson
Product Marketing Manager, Network Detection & Response
autonomous investigations, endpoint, ndr, network detection and responseDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
23
Oct 2025

Darktrace delivers the next evolution of unified and proactive NDR

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

The combined context of native network and endpoint process data significantly reduces incident triage and investigation times for threats spanning both domains. Our business-centric approach learns what normal looks like for each endpoint, and now uses process context to extend our ability to identify novel threats that existing EDR/XDR tools often  miss.

Summary of what’s new:

  • Native endpoint process telemetry combined with NDR, bridging the EDR gap
  • Self-Learning AI on the endpoint to stop novel threats missed by EDR
  • Sophisticated Agentic AI to automate SecOps investigations across all major IT domains
  • AI-native, real-time threat detection, investigation, and response (TDIR) for cross-domain activity throughout the enterprise

Why is this an important next step in NDR?

Security analysts are buried under a flood of alerts that lack the context needed to separate genuine threats from noise. The root problem is that most security tools only see one slice of the environment. IT and OT networks, endpoints, and cloud systems are monitored in isolation, with little correlation between them.

As a result, investigations are highly manual. Analysts are forced to pivot between siloed point-products, each providing only a fragment of the incident. This slows response, creates blind spots, and limits the team’s ability to understand and contain threats effectively.

In many cases, the high degree of skill it takes to pivot tools and conduct investigations leads even the most experienced analysts closer to burnout, especially when they are already exhausted by the quantity of alerts. Ultimately, the human personnel managing these systems are using their skills to accommodate for the lack of synergy between tools they are using in their security stack, rather than developing the higher-value expertise needed to anticipate, prevent, and respond to emerging threats.

Many organizations have attempted to overcome this challenge by implementing XDR solutions. But, XDR does not cover NDR related use cases. This is especially true in OT/CPS environments where it is not possible to install an agent on devices.

XDR is an Endpoint-focused tool that cannot see the full picture of threats moving laterally across the network, targeting unmanaged devices, or blending into legitimate traffic. While XDR is still a strong tool in the arsenal, attackers are noticing where the gaps are:

  • A CISA Red Team assessment found that one U.S. critical infrastructure organization suffered prolonged compromise because it overly relied on host‑based EDR and lacked sufficient network-layer defenses.  

Bottom line: Without native network detection and response (NDR), critical incidents slip through undetected.

Not all NDR tools are built the same

When it comes to NDR, the details matter. Here are a few reasons why not all NDR solutions are created equal:

  • Most NDR solutions depend on EDR/XDR integrations to ingest endpoint alerts, which are raised based on activity that is already known to be malicious
  • They can’t investigate beyond what the EDR already flags, lacking process-level context in network investigations
  • Almost no NDR solutions have a native endpoint agent to extend NDR visibility to remote worker devices

This reliance on EDR leaves critical gaps in network coverage, since EDRs themselves don’t provide network-level visibility.

The NEXT evolution of NDR

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

The combined context of native network and endpoint process data significantly reduces incident triage and investigation times for threats spanning both domains, our business-centric approach with new data also extends our ability to identify novel threats that existing EDR/XDR may miss.

Darktrace / ENDPOINT agents are now able to utilize new Network Endpoint eXtended Telemetry (NEXT) capabilities. This combines full network visibility with native endpoint process data, enabling autonomous investigations that trace threats from initial network activity all the way to the root cause at the endpoint, without manual correlation or tool switching. This bridges the gap between NDR and the endpoint, while adding value to existing EDR investments.

Darktrace natively shows the endpoint process context in relation to network events, complete with parent/child process relationships, adding immediate context to network investigations without needing to pivot to your EDR.
Figure 1: Darktrace natively shows the endpoint process context in relation to network events, complete with parent/child process relationships, adding immediate context to network investigations without needing to pivot to your EDR.

Leveraging this data in investigations

This additional context is then leveraged by Cyber AI Analyst, a sophisticated agentic AI system that autonomously performs end-to-end investigations of all relevant alerts and prioritizes incidents. With the new endpoint process visibility, Cyber AI Analyst now incorporates process context into its decision-making, which improves detection accuracy, filters out benign activity, and enhances incident narratives with process-level insights.

This makes Darktrace the first NDR to natively investigate threats across network and endpoint telemetry with an autonomous, agentic AI analyst. And with our Self-Learning AI, Darktrace continuously evolves by understanding what’s normal for each unique environment, now adding process data to extend visibility and range of detections. This enables Darktrace to detect and contain novel threats, including zero-days, insider threats, and emerging attack techniques, up to 8 days before public disclosure.

This is more than a solution to a visibility problem. It’s a fundamental evolution in how threats are detected, investigated, and stopped. By applying agentic AI, Darktrace empowers security teams to move from reactive alert triage to proactive, autonomous defense, surfacing and blocking threats that others simply can’t see.

An excerpt from a Darktrace Cyber AI Analyst incident, showing the inclusion of native endpoint process context alongside other network events.
Figure 2: An excerpt from a Darktrace Cyber AI Analyst incident, showing the inclusion of native endpoint process context alongside other network events.

Continued innovation in detection and response

Darktrace also continues to invest in our core NDR capabilities, delivering enhancements and innovations to solve modern network security challenges. In the latest release, Darktrace / NETWORK has been enhanced to increase detection efficacy and performance. This includes increased protocol detection fidelity and new support for custom port mappings, plus expanded visibility into HTTP traffic to support more targeted threat hunting across a wider range of application layer activity. In addition, vSensor performance has been upgraded for tunnel protocols such as Geneve.

We have also released enhancements to Autonomous Response, which is already trusted by thousands of organizations to contain threats at the earliest stages without causing business disruption. This includes enhanced support for highly complex and segmented networks, plus the ability to extend Autonomous Response actions to more areas with additional firewall integration support. This enables faster and more effective response to network threats, and continues Darktrace’s proven ability to contain zero-day threats up to 8 days before public disclosure.

Providing seamless operations with the new Darktrace ActiveAI Security Portal

As part of Darktrace’s commitment to breaking down silos across the cyber defense lifecycle, this release also introduces major platform enhancements that tackle often-overlooked operational gaps specifically around user access, permissions, and integration workflows. With the launch of the new Darktrace ActiveAI Security Portal, organizations can now manage security at scale across diverse environments, making it ideal for large enterprises, MSSPs, and partners overseeing multiple tenants. These updates ensure that visibility, control, and scalability extend beyond detection and response and into how teams manage and interact with the platform itself.

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. Innovations to our suite of Exposure Management & Attack Surface Management products including:

  • 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.
  • No-Telemetry Endpoint: Collects installed software data and maps it to known CVEs—without network traffic—providing device-level vulnerability context and operational relevance.
  • Cost-Benefit Analysis for Patching: Calculates ROI by comparing patching effort with potential exploit impact, factoring in headcount time, device count, patch difficulty, and automation availability.

Visit these blogs to learn more about updates:

As attackers exploit gaps between tools, the Darktrace ActiveAI Security Platform delivers unified detection, automated investigation, and autonomous response across cloud, endpoint, email, network, and OT. With full-stack visibility and AI-native workflows, Darktrace empowers security teams to detect, understand, and stop novel threats before they escalate.

Join our Live Launch Event

When? 

December 9, 2025

What will be covered?

Join our live broadcast to experience how Darktrace is eliminating blind spots for detection and response across your complete enterprise with new innovations in Agentic AI across our ActiveAI Security platform. Industry leaders from IDC will join Darktrace customers to discuss challenges in cross-domain security, with a live walkthrough reshaping the future of Network Detection & Response, Endpoint Detection & Response, Email Security, and SecOps in novel threat detection and autonomous investigations.

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
Mikey Anderson
Product Marketing Manager, Network Detection & Response

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December 23, 2025

How to Secure AI in the Enterprise: A Practical Framework for Models, Data, and Agents

How to secure AI in the enterprise: A practical framework for models, data, and agents Default blog imageDefault blog image

Introduction: Why securing AI is now a security priority

AI adoption is at the forefront of the digital movement in businesses, outpacing the rate at which IT and security professionals can set up governance models and security parameters. Adopting Generative AI chatbots, autonomous agents, and AI-enabled SaaS tools promises efficiency and speed but also introduces new forms of risk that traditional security controls were never designed to manage. For many organizations, the first challenge is not whether AI should be secured, but what “securing AI” actually means in practice. Is it about protecting models? Governing data? Monitoring outputs? Or controlling how AI agents behave once deployed?  

While demand for adoption increases, securing AI use in the enterprise is still an abstract concept to many and operationalizing its use goes far beyond just having visibility. Practitioners need to also consider how AI is sourced, built, deployed, used, and governed across the enterprise.

The goal for security teams: Implement a clear, lifecycle-based AI security framework. This blog will demonstrate the variety of AI use cases that should be considered when developing this framework and how to frame this conversation to non-technical audiences.  

What does “securing AI” actually mean?

Securing AI is often framed as an extension of existing security disciplines. In practice, this assumption can cause confusion.

Traditional security functions are built around relatively stable boundaries. Application security focuses on code and logic. Cloud security governs infrastructure and identity. Data security protects sensitive information at rest and in motion. Identity security controls who can access systems and services. Each function has clear ownership, established tooling, and well-understood failure modes.

AI does not fit neatly into any of these categories. An AI system is simultaneously:

  • An application that executes logic
  • A data processor that ingests and generates sensitive information
  • A decision-making layer that influences or automates actions
  • A dynamic system that changes behavior over time

As a result, the security risks introduced by AI cuts across multiple domains at once. A single AI interaction can involve identity misuse, data exposure, application logic abuse, and supply chain risk all within the same workflow. This is where the traditional lines between security functions begin to blur.

For example, a malicious prompt submitted by an authorized user is not a classic identity breach, yet it can trigger data leakage or unauthorized actions. An AI agent calling an external service may appear as legitimate application behavior, even as it violates data sovereignty or compliance requirements. AI-generated code may pass standard development checks while introducing subtle vulnerabilities or compromised dependencies.

In each case, no single security team “owns” the risk outright.

This is why securing AI cannot be reduced to model safety, governance policies, or perimeter controls alone. It requires a shared security lens that spans development, operations, data handling, and user interaction. Securing AI means understanding not just whether systems are accessed securely, but whether they are being used, trained, and allowed to act in ways that align with business intent and risk tolerance.

At its core, securing AI is about restoring clarity in environments where accountability can quickly blur. It is about knowing where AI exists, how it behaves, what it is allowed to do, and how its decisions affect the wider enterprise. Without this clarity, AI becomes a force multiplier for both productivity and risk.

The five categories of AI risk in the enterprise

A practical way to approach AI security is to organize risk around how AI is used and where it operates. The framework below defines five categories of AI risk, each aligned to a distinct layer of the enterprise AI ecosystem  

How to Secure AI in the Enterprise:

  • Defending against misuse and emergent behaviors
  • Monitoring and controlling AI in operation
  • Protecting AI development and infrastructure
  • Securing the AI supply chain
  • Strengthening readiness and oversight

Together, these categories provide a structured lens for understanding how AI risk manifests and where security teams should focus their efforts.

1. Defending against misuse and emergent AI behaviors

Generative AI systems and agents can be manipulated in ways that bypass traditional controls. Even when access is authorized, AI can be misused, repurposed, or influenced through carefully crafted prompts and interactions.

Key risks include:

  • Malicious prompt injection designed to coerce unwanted actions
  • Unauthorized or unintended use cases that bypass guardrails
  • Exposure of sensitive data through prompt histories
  • Hallucinated or malicious outputs that influence human behavior

Unlike traditional applications, AI systems can produce harmful outcomes without being explicitly compromised. Securing this layer requires monitoring intent, not just access. Security teams need visibility into how AI systems are being prompted, how outputs are consumed, and whether usage aligns with approved business purposes

2. Monitoring and controlling AI in operation

Once deployed, AI agents operate at machine speed and scale. They can initiate actions, exchange data, and interact with other systems with little human oversight. This makes runtime visibility critical.

Operational AI risks include:

  • Agents using permissions in unintended ways
  • Uncontrolled outbound connections to external services or agents
  • Loss of forensic visibility into ephemeral AI components
  • Non-compliant data transmission across jurisdictions

Securing AI in operation requires real-time monitoring of agent behavior, centralized control points such as AI gateways, and the ability to capture agent state for investigation. Without these capabilities, security teams may be blind to how AI systems behave once live, particularly in cloud-native or regulated environments.

3. Protecting AI development and infrastructure

Many AI risks are introduced long before deployment. Development pipelines, infrastructure configurations, and architectural decisions all influence the security posture of AI systems.

Common risks include:

  • Misconfigured permissions and guardrails
  • Insecure or overly complex agent architectures
  • Infrastructure-as-Code introducing silent misconfigurations
  • Vulnerabilities in AI-generated code and dependencies

AI-generated code adds a new dimension of risk, as hallucinated packages or insecure logic may be harder to detect and debug than human-written code. Securing AI development means applying security controls early, including static analysis, architectural review, and continuous configuration monitoring throughout the build process.

4. Securing the AI supply chain

AI supply chains are often opaque. Models, datasets, dependencies, and services may come from third parties with varying levels of transparency and assurance.

Key supply chain risks include:

  • Shadow AI tools used outside approved controls
  • External AI agents granted internal access
  • Suppliers applying AI to enterprise data without disclosure
  • Compromised models, training data, or dependencies

Securing the AI supply chain requires discovering where AI is used, validating the provenance and licensing of models and data, and assessing how suppliers process and protect enterprise information. Without this visibility, organizations risk data leakage, regulatory exposure, and downstream compromise through trusted integrations.

5. Strengthening readiness and oversight

Even with strong technical controls, AI security fails without governance, testing, and trained teams. AI introduces new incident scenarios that many security teams are not yet prepared to handle.

Oversight risks include:

  • Lack of meaningful AI risk reporting
  • Untested AI systems in production
  • Security teams untrained in AI-specific threats

Organizations need AI-aware reporting, red and purple team exercises that include AI systems, and ongoing training to build operational readiness. These capabilities ensure AI risks are understood, tested, and continuously improved, rather than discovered during a live incident.

Reframing AI security for the boardroom

AI security is not just a technical issue. It is a trust, accountability, and resilience issue. Boards want assurance that AI-driven decisions are reliable, explainable, and protected from tampering.

Effective communication with leadership focuses on:

  • Trust: confidence in data integrity, model behavior, and outputs
  • Accountability: clear ownership across teams and suppliers
  • Resilience: the ability to operate, audit, and adapt under attack or regulation

Mapping AI security efforts to recognized frameworks such as ISO/IEC 42001 and the NIST AI Risk Management Framework helps demonstrate maturity and aligns AI security with broader governance objectives.

Conclusion: Securing AI is a lifecycle challenge

The same characteristics that make AI transformative also make it difficult to secure. AI systems blur traditional boundaries between software, users, and decision-making, expanding the attack surface in subtle but significant ways.

Securing AI requires restoring clarity. Knowing where AI exists, how it behaves, who controls it, and how it is governed. A framework-based approach allows organizations to innovate with AI while maintaining trust, accountability, and control.

The journey to secure AI is ongoing, but it begins with understanding the risks across the full AI lifecycle and building security practices that evolve alongside the technology.

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About the author
Brittany Woodsmall
Product Marketing Manager, AI & Attack Surface

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December 22, 2025

The Year Ahead: AI Cybersecurity Trends to Watch in 2026

2026 cyber threat trendsDefault blog imageDefault blog image

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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