Blog
/
Cloud
/
September 25, 2025

Introducing the Industry’s First Truly Automated Cloud Forensics Solution

The launch of Darktrace / Forensic Acquisition & Investigation marks a breakthrough moment for cloud security, bringing automated forensic investigations — once reserved for the largest organizations and specialized DFIR teams — to security teams of every size.
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
Paul Bottomley
Director of Product Management | Darktrace
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
25
Sep 2025

Why Cloud Investigations Fail Today

Cloud investigations have become one of the hardest problems in modern cybersecurity. Traditional DFIR tools were built for static, on-prem environments, rather than dynamic and highly scalable cloud environments, containing ephemeral workloads that disappear in minutes. SOC analysts are flooded with cloud security alerts with one-third lacking actionable data to confirm or dismiss a threat[1], while DFIR teams waste 3-5 days requesting access and performing manual collection, or relying on external responders.

These delays leave organizations vulnerable. Research shows that nearly 90% of organizations suffer some level of damage before they can fully investigate and contain a cloud incident [2]. The result is a broken model: alerts are closed without a complete understanding of the threat due to a lack of visibility and control, investigations drag on, and attackers retain the upper hand.

For SOC teams, the challenge is scale and clarity. Analysts are inundated with alerts but lack the forensic depth to quickly distinguish real threats from noise. Manual triage wastes valuable time, creates alert fatigue, and often forces teams to escalate or dismiss incidents without confidence — leaving adversaries with room to maneuver.

For DFIR teams, the challenge is depth and speed. Traditional forensics tools were built for static, on-premises environments and cannot keep pace with ephemeral workloads that vanish in minutes. Investigators are left chasing snapshots, requesting access from cloud teams, or depending on external responders, leading to blind spots and delayed response.

That’s why we built Darktrace / Forensic Acquisition & Investigation, the first automated forensic solution designed specifically for the speed, scale, and complexities of the cloud. It addresses both sets of challenges by combining automated forensic evidence capture, attacker timeline reconstruction, and cross-cloud scale. The solution empowers SOC analysts with instant clarity and DFIR teams with forensic depth, all in minutes, not days. By leveraging the very nature of the cloud, Darktrace makes these advanced capabilities accessible to security teams of all sizes, regardless of expertise or resources.

Introducing Automated Forensics at the Speed and Scale of Cloud

Darktrace / Forensic Acquisition & Investigation transforms cloud investigations by capturing, processing, and analyzing forensic evidence of cloud workloads, instantly, even from time-restricted ephemeral resources. Triggered by a detection from any cloud security tool, the entire process is automated, providing accurate root cause analysis and deep insights into attacker behavior in minutes rather than days or weeks. SOC and DFIR teams no longer have to rely on manual processes, snapshots, or external responders, they can now leverage the scale and elasticity of the cloud to accelerate triage and investigations.

Seamless Integration with Existing Detection Tools

Darktrace / Forensic Acquisition & Investigation does not require customers to replace their detection stack. Instead, it integrates with cloud-native providers, XDR platforms, and SIEM/SOAR tools, automatically initiating forensic capture whenever an alert is raised. This means teams can continue leveraging their existing investments while gaining the forensic depth required to validate alerts, confirm root cause, and accelerate response.

Most importantly, the solution is natively integrated with Darktrace / CLOUD, turning real-time detections of novel attacker behaviors into full forensic investigations instantly. When Darktrace / CLOUD identifies suspicious activity such as lateral movement, privilege escalation, or abnormal usage of compute resources, Darktrace / Forensic Acquisition & Investigation automatically preserves the underlying forensic evidence before it disappears. This seamless workflow unites detection, response, and investigation in a way that eliminates gaps, accelerates triage, and gives teams confidence that every critical cloud alert can be investigated to completion.

Figure 1: Integration with Darktrace / CLOUD – this example is showing the ability to pivot into the forensic investigation associated with a compromised cloud asset

Automated Evidence Collection Across Hybrid and Multi-Cloud

The solution provides automated forensic acquisition across AWS, Microsoft Azure, GCP, and on-prem environments. It supports both full volume capture, creating a bit-by-bit copy of an entire storage device for the most comprehensive preservation of evidence, and triage collection, which prioritizes speed by gathering only the most essential forensic artifacts such as process data, logs, network connections, and open file contents. This flexibility allows teams to strike the right balance between speed and depth depending on the investigation at hand.

Figure 2: Ability to acquire forensic data from Cloud, SaaS and on-prem environments

Automated Investigations, Root Cause Analysis and Attacker Timelines

Once evidence is collected, Darktrace applies automation to reconstruct attacker activity into a unified timeline. This includes correlating commands, files, lateral movement, and network activity into a single investigative view enriched with custom threat intelligence such as IOCs. Detailed investigation reporting including an investigation summary, an overview of the attacker timeline, and key events. Analysts can pivot into detailed views such as the filesystem view, traversing directories or inspecting file content, or filter and search using faceted options to quickly narrow the scope of an investigation.

Figure 3: Automated Investigation view surfacing the most significant attacker activity, which is contextualized with Alarm information

Forensics for Containers and Ephemeral Assets

Investigating containers and serverless workloads has historically been one of the hardest challenges for DFIR teams, as these assets often disappear before evidence can be preserved. Darktrace / Forensic Acquisition & Investigation captures forensic evidence across managed Kubernetes cloud services, even from distroless or no-shell containers, AWS ECS and other environments, ensuring that ephemeral activity is no longer a blind spot. For hybrid organizations, this extends to on-premises Kubernetes and OpenShift deployments, bringing consistency across environments.

Figure 4: Container investigations – this example is showing the ability to capture containers from managed Kubernetes cloud services

SaaS Log Collection for Modern Investigations

Beyond infrastructure-level data, the solution collects logs from SaaS providers such as Microsoft 365, Entra ID, and Google Workspace. This enables investigations into common attack types like business email compromise (BEC), account takeover (ATO), and insider threats — giving teams visibility into both infrastructure-level and SaaS-driven compromise from a single platform.

Figure 5: Ability to import logs from SaaS providers including Microsoft 365, Entra ID, and Google Workspace

Proactive Vulnerability and Malware Discovery

Finally, the solution surfaces risk proactively with vulnerability and malware discovery for Linux-based cloud resources. Vulnerabilities are presented in a searchable table and correlated with the attacker timeline, enabling teams to quickly understand not just which packages are exposed, but whether they have been targeted or exploited in the context of an incident.

Figure 6: Vulnerability data with pivot points into the attacker timeline

Cloud-Native Scale and Performance

Darktrace / Forensic Acquisition & Investigation uses a cloud-native parallel processing architecture that spins up compute resources on demand, ensuring that investigations run at scale without bottlenecks. Detailed reporting and summaries are automatically generated, giving teams a clear record of the investigation process and supporting compliance, litigation readiness, and executive reporting needs.

Scalable and Flexible Deployment Options

Every organization has different requirements for speed, control, and integration. Darktrace / Forensic Acquisition & Investigation is designed to meet those needs with two flexible deployment models.

  • Self-Hosted Virtual Appliance delivers deep integration and control across hybrid environments, preserving forensic data for compliance and litigation while scaling to the largest enterprise investigations.
  • SaaS-Delivered Deployment provides fast time-to-value out of the box, enabling automated forensic response without requiring deep cloud expertise or heavy setup.

Both models are built to scale across regions and accounts, ensuring organizations of any size can achieve rapid value and adapt the solution to their unique operational and compliance needs. This flexibility makes advanced cloud forensics accessible to every security team — whether they are optimizing for speed, integration depth, or regulatory alignment

Delivering Advanced Cloud Forensics for Every Team

Until now, forensic investigations were slow, manual, and reserved for only the largest organizations with specialized DFIR expertise. Darktrace / Forensic Acquisition & Investigation changes that by leveraging the scale and elasticity of the cloud itself to automate the entire investigation process. From capturing full disk and memory at detection to reconstructing attacker timelines in minutes, the solution turns fragmented workflows into streamlined investigations available to every team.

Whether deployed as a SaaS-delivered service for fast time-to-value or as a self-hosted appliance for deep integration, Darktrace / Forensic Acquisition & Investigation provides the features that matter most: automated evidence capture, cross-cloud investigations, forensic depth for ephemeral assets, and root cause clarity without manual effort.

With Darktrace / Forensic Acquisition & Investigation, what once took days now takes minutes. Now, forensic investigations in the cloud are faster, more scalable, and finally accessible to every security team, no matter their size or expertise.

[related-resource]

Sources: [1], [2] Darktrace Report: Organizations Require a New Approach to Handle Investigations in the Cloud

Additional Resources

Darktrace Innovation Launch: Automated Cloud Forensics

Discover the industry's first truly automated cloud forensics solution in this live broadcast with experts from AWS and Forrester.

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
Paul Bottomley
Director of Product Management | Darktrace

More in this series

No items found.

Blog

/

/

January 6, 2026

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

Default blog imageDefault blog image

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.

Continue reading
About the author
Kelland Goodin
Product Marketing Specialist

Blog

/

AI

/

January 5, 2026

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.

Continue reading
About the author
Brittany Woodsmall
Product Marketing Manager, AI & Attack Surface
Your data. Our AI.
Elevate your network security with Darktrace AI