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May 22, 2020

Securing AWS Cloud Environments

Discover how self-learning AI in AWS environments detects and beats threats early with enterprise-wide analysis.
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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.
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22
May 2020

Cloud platforms transform the way we build digital infrastructure, allowing us to create incredibly innovative environments for business – but often, it’s at the cost of visibility and control.

With complex hybrid and multi-cloud infrastructures becoming an essential part of increasingly diverse digital estates, the journey to the cloud has fundamentally reshaped the traditional paradigm of the network perimeter, while expanding the attack surface at an alarming rate. Meanwhile, traditional security controls still only offer point solutions that rely on retrospective rules and threat signatures and fail to stop novel and advanced attacks.

To shoulder the weight of shared responsibility for cloud security, organizations require the approach offered by Darktrace DETECT & RESPOND. With Self-Learning AI, DETECT continuously learns what normal ‘patterns of life’ look like for every user, device, virtual machine, and container across an organization. By actively developing a bespoke understanding of ‘self,’ the DETECT can identify the subtle anomalies that point to an advanced attack, without any pre-defined assumptions of ‘good’ or ‘bad' and RESPOND can autonomously interfere to stop emerging threats without disrupting business operations.

As more and more businesses turn to AWS to leverage the benefits of cloud infrastructure, gaining visibility and security for AWS-hosted data and applications is absolutely crucial. The advent of AWS VPC traffic mirroring has allowed Darktrace to shine a light on blind spots in our customers’ AWS environments, ensuring that our Cyber AI security platform can stop any type of threat that emerges. With the AI-powered security securing your AWS environment, you can embrace all the benefits of the cloud with confidence.

Self-learning Cyber AI with granular, real-time visibility

VPC traffic mirroring gives our Self-Learning AI access to granular packet data, allowing DETECT to extract hundreds of features from the raw data and build rich behavioral models for our customers’ AWS cloud environments. This real-time visibility to the underlying fabric of AWS environments provided by VPC traffic mirroring helps Darktrace Cyber AI learn ‘on the job,’ continuously adapting as your business evolves. Darktrace provides the only security solution that learns in real time, a critical feature given the speed and scale of development in the cloud.

Unified control: Correlating patterns across infrastructure

Taking a fundamentally unique approach, DETECT actively correlates activity across AWS and beyond – whether your digital ecosystem includes other cloud environments, SaaS applications, or any range of on- and off-premise infrastructure. From a threat detection perspective, this is crucial, as security events detected in one part of an organization are often part of a broader security incident. This ensures that threats in the cloud are not siloed from monitoring of the rest of the infrastructure, nor are the implications for cloud security ignored when intrusions occur elsewhere in the network.

Neutralizing sophisticated and novel attacks

Legacy security controls miss novel and advanced attacks targeting cloud infrastructure. With VPC traffic mirroring supporting Darktrace Cyber AI’s understanding of an organization’s AWS environment, any slight changes from normal behavior that may indicate a potential threat can be detected immediately. This allows the DETECT to catch the full range of cloud-based attacks, from zero-day malware, to stealthy insider threats.

“Darktrace represents a new frontier in AI-based cyber defense. Our team now has complete real-time coverage across our SaaS applications and cloud containers.”

— CIO, City of Las Vegas

How it works: Using VPC traffic mirroring to analyze AWS traffic

For customers leveraging AWS within an IaaS model, Darktrace uses VPC traffic mirroring to collect metadata from mirrored VPC packets in a Darktrace probe known as a ‘vSensor’. The vSensor captures real-time traffic and selectively forwards relevant metadata to a Darktrace cloud instance or on-premise probe. From here, DETECT correlates VPC traffic with cloud, email, network, and SaaS traffic across a customer’s hybrid and multi-cloud infrastructure for analysis.

By utilizing VPC traffic mirroring in this way, the Immune System can perform deep packet inspection on traffic in the customer’s AWS cloud environment, up to and including the application layer. Hundreds of features are extracted from the raw data, ranging from high-level metrics of data flow quantities, to peer relationship meta-data, to specific application layer events. These features allow Darktrace Cyber AI to build rich behavioral models that let it understand normal patterns of life for the organization and detect malicious activity. It is important that Darktrace is able to construct these metrics from the raw data rather than relying on flow logs alone, as flow logs don't provide the required level of granularity or real-time events within connections.

For non-Nitro AWS instances, we deploy lightweight agents known as ‘OS-Sensors’ that feed relevant traffic to a local vSensor and, in turn, to a Darktrace cloud instance or on-premise probe. Once configured, OS-Sensors can easily be scaled as new instances are spun up. Darktrace also offers a specialized OS-Sensor that provides coverage in containerized systems like Docker and Kubernetes.

Richer context with AWS CloudTrail logs

In addition to analyzing data with VPC traffic mirroring, the DETECT also monitors management and data events within AWS. It does so via HTTP requests for logfiles generated by AWS CloudTrail, which monitors events from all AWS services, including:

  • EC2
  • IAM
  • S3
  • VPC
  • Lambda

Different event types produced via CloudTrail are organized by Darktrace into categories based on the action type and the AWS services that generate it. These different categories show up as metrics in the DETECT user interface, the Threat Visualizer. This information is used to provide even richer context in connection with mirrored traffic in VPCs, as well as all cloud, network, email, and SaaS traffic across a customer’s entire digital environment.

Darktrace deployment scenarios for AWS customers

For IaaS environments, Darktrace deploys a vSensor in each cloud environment. Within AWS environments, the vSensor captures real-time traffic with AWS VPC traffic mirroring. The receiving vSensor processes the data and feeds it back to the cloud-based Darktrace instance. AWS customers additionally have the option of deploying a ‘Darktrace Security Module’ to monitor IaaS management and data events at the API level, such as logins, editing virtual servers, or creating new access credentials.

Figure 1: A cloud-only deployment scenario — Darktrace manages a master cloud probe which receives traffic from sensors and connectors in IaaS and/or SaaS environments.

For hybrid IaaS deployments, Darktrace will similarly deploy vSensors, and OS-Sensors as appropriate. Cloud traffic and event data from AWS and any other cloud environments is then fed to a Darktrace probe in the cloud or on-premise network. For the latter scenario, Darktrace will deploy a physical appliance that ingests real-time network traffic via a SPAN port or network tap, allowing it to correlate patterns across the entire digital ecosystem.

Figure 2: A hybrid cloud deployment scenario, with multi-cloud infrastructure across AWS, Azure and GCP

For hybrid SaaS deployments, Darktrace will deploy provider-specific Darktrace Security Modules on either a physical or cloud-based Darktrace probe, in addition to any other relevant vSensors and OS-Sensors in place. SaaS data is then analyzed and correlated with traffic and user behaviors across AWS, other cloud environments, and any on- and off- premise cyber-physical infrastructure.

Figure 3: A hybrid SaaS deployment scenario

Defense against the full range of threats in the cloud

With the deep insight and powerful reaction capabilities of Cyber AI, Darktrace DETECT & RESPOND are the only proven technologies to stop the full range of cyber-threats in the cloud, including:

  • Critical misconfigurations
  • Insider threat
  • Compromised credentials
  • Novel and advanced malware
  • Password brute-force attacks
  • Data exfiltration
  • Lateral movement
  • Man-in-the-middle attacks
  • Crypto-jacking
  • Violations of policy

Case Studies

Crypto mining malware inadvertently installed

Darktrace detected a mistake from a junior DevOps engineer in a multinational organization with workloads across AWS and Azure and leveraging containerized systems like Docker and Kubernetes. The engineer accidentally downloaded an update that included a crypto miner, which led to an infection across multiple cloud production systems.

After the initial infection, the malware started beaconing out to an external command and control server, which was immediately picked up by Darktrace. With the external connection established and the attack mission instructions delivered, the crypto malware infection was then able to rapidly spread across the organization’s expansive cloud infrastructure at machine speed, infecting 20 cloud servers in under 15 seconds.

Extensive visibility into the organization’s AWS environment via VPC traffic mirroring was a key factor allowing Darktrace Cyber AI to identify the scale of the attack. With the dynamic and unified view across the company’s sprawling hybrid and multi-cloud infrastructure provided by Darktrace, the company’s security team was able to contain the attack within minutes, rather than hours or days. Even though the attack moved at machine speed, by leveraging solutions like VPC traffic mirroring to continuously analyze behavior in the cloud, Darktrace caught the threat at an early enough stage – well before the costs could start to mount.

Developer misuse of AWS cloud infrastructure

At an insurance group, a DevOps Engineer was attempting to build a parallel back-up infrastructure within AWS to replicate the organization’s data center production systems. The technical implementation was perfect, and the back-up systems were created – however, the cost of running the system would have been several million dollars per year.

The DevOps Engineer was unaware of the costs associated with the project and kept management in the dark. The cloud infrastructure was launched, and the costs started rising. Yet with real-time access to the company’s AWS environment provided by VPC traffic mirroring, Darktrace’s Cyber AI was immediately alerted to this unusual behavior, allowing the security team to take preventative action immediately.

With Darktrace Cyber AI, embrace the benefits of AWS

As organizations increasingly turn to the cloud and the threat surface continues to expand, security teams need self-learning AI on their side to gain the strongest insights, illuminate every blind spot, and stop all attacks.

By providing an enterprise-wide Cyber AI platform, Darktrace helps teams overcome the traditional security challenge of manually piecing together incidents across disparate corners of an organization. The unified visibility and control offered by Darktrace PREVENT, DETECTRESPOND, & HEAL reduces the complexity and dashboard fatigue that many teams continue to struggle with, while the system’s multi-dimensional insight enhances its decision-making and threat confidence. Darktrace further augments this process with the Immune System’s AI Analyst capability, which takes the additional step of automatically investigating threats detected by Darktrace and producing concise, AI-generated reports that communicate the full scope of an incident.

With the granular, real-time visibility of VPC traffic mirroring Darktrace, you can be certain your AWS cloud environments are always protected.

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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.
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July 24, 2026

Darktrace / EMAIL Expands Behavioral Defense Across Email and Collaboration Workflows

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Email and collaboration tools do more than carry messages. They are where organizations approve payments, share sensitive data, reset credentials, and make thousands of everyday decisions. Increasingly, they are interfaces through which humans direct AI agents in their daily activity. Email, Slack and Teams are high volume, rich with sensitive data, and an easy place to hide malicious activity.

The opportunity isn’t lost on bad actors. Darktrace / EMAIL detected more than 32 million high-confidence phishing emails globally in 2025, and 70% of those messages passed DMARC authentication.  Phishing is increasingly difficult to detect and familiar trust signals alone are not enough. People and security teams need to understand how a message fits the normal behavior of the sender, recipient, and organization. They also need to correlate activity across platforms to spot threats that span multiple channels.

To effectively secure against today’s evolved threats, security teams need to act at two levels: they need to help each employee make a safer decision ‘in the moment’, and they need to understand the wider patterns that may expose the business to risk.

Darktrace is introducing four new capabilities in Darktrace / EMAIL to address both challenges. The new features explain suspicious content more clearly to end users, strengthen the capabilities of Darktrace / Adaptive Human Defense with richer guidance, let organizations define their own patterns for detecting sensitive data in messages, and give security teams a process-level view of risk across email and collaboration workflows.

Darktrace / EMAIL Inbox Analysis highlights risky content within your emails

A warning is more useful when it explains what the user should look at. To help do that, we’ve expanded Darktrace / EMAIL’s Inbox Analysis Add-In to highlight potentially dangerous content within the body of emails that Darktrace / EMAIL flags as potentially suspicious or high risk.  

The add-in can highlight language designed to create urgency, financial references, requests for payment, suspicious links, and content that is unusual for the sender. Each highlighted element includes a pop up that explains why it may be suspicious. Instead of asking an employee to accept a verdict without context, the analysis helps them examine the message and make a more informed decision.

Enhanced Just-In-Time Training Banners in Darktrace / Adaptive Human Defense

Enhanced Just-In-Time Training Banners build on the same principle. The banners now include a contextual header, actionable advice, and specific detection context. This gives employees more useful guidance at the point of risk without adding unnecessary information or cognitive load.

Together, the capabilities help turn a warning into a short learning moment. Employees can see what looks unusual, understand what action to take, and build their judgment.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention

Sensitive data is different for every business. Standard categories such as payment card details or government identifiers matter, but organizations also have their own customer codes, project names, research formats, account structures, and internal identifiers.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention allows administrators to write custom expressions for the data their organization needs to protect. Matched content can trigger existing model actions and data loss prevention (DLP) workflows, extending Darktrace's DLP capabilities.

This extends data loss detection beyond a fixed library of common data types. Security teams can apply controls to information that is sensitive in the context of their own organization and adapt those controls as the business changes.

Introducing Email and Collaboration Workflow Risk Posture Dashboards

Some of the most important risks are not isolated events. They are repeated ways of working that create an opening for error, misuse, or attack. For example, a payment request may be one suspicious message, but a recurring approval workflow that relies on weak verification is a business process risk.

The new Email and Collaboration Workflow Risk Posture Dashboard analyzes email and collaboration data across Email, Microsoft Teams, Slack and Zoom to provide a process-level view of risk in the organization. These may include financial authorization workflows, sensitive data sharing patterns, and activity that could expose credentials.

The dashboard brings these patterns into a view and provides actionable recommendations. This helps security teams determine where to investigate or strengthen controls, where ownership needs to be clarified, and where the business may need to change a risky process. It gives CISOs a clearer view of how human and communication risk is embedded in everyday operations, not only where individual alerts occur.

Behavior connects the individual decision to the wider risk

These capabilities build on Darktrace’s unique behavioral approach to security. We use Adaptive AI to learn how people and AI normally behave within an organization, creating the context needed to recognize when activity changes.

Within the Darktrace Behavioral Defense Platform, Darktrace / EMAIL helps protect people against phishing, account takeover, data exfiltration, and human risk across email and collaboration tools. The new capabilities extend that protection in both directions. They give employees clearer context for the decision in front of them, while giving security leaders a broader view of the workflows and behavior that create risk across the organization.

The result is not simply more alerts. It is a better understanding of why something is risky, what action to take, and where the organization can reduce risk before a familiar process becomes an easy route for an attacker.

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Carlos Gray
Senior Product Marketing Manager, Email

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July 24, 2026

When Guardrails Break: Why Securing AI Requires Behavioral Detection and Autonomous Containment

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Bottom line up front: Governance, guardrails, identity controls, and secure development are necessary to secure AI, but they are not sufficient. AI systems are probabilistic, adaptive, and non-deterministic. Therefore, organizations need two critical layers of security:

  1. Behavioral-based detection that can identify when AI begins to act outside its intended purpose; and  
  2. Surgical, explainable autonomous containment that can stop risky activity before it causes material damage.  

That capability depends on multiple specialized AI models working together, not one LLM making every decision.

Organizations are embedding AI into development, business operations, and security workflows faster than most security programs can adapt. The risk is no longer limited to the model. It extends across prompts, data, identities, agents, memory, APIs, tools, permissions, and the trust relationships connecting them.

In my recent blog, Securing AI: Analysis of the Complete Security Stack with Governance and Controls, I outlined a defense-in-depth strategy spanning governance, identity, data security, secure development, runtime detection, autonomous containment, and recovery. The most urgent requirement across that architecture is the ability to understand how AI behaves in practice and contain it when that behavior becomes risky.  

Why non-deterministic systems require behavioral-based detection

Traditional controls remain foundational. Organizations need least privilege, strong identity controls, secure-by-design architecture, data governance, AI inventories, guardrails, testing, and clear boundaries on autonomy.

But deterministic controls, which assume predictable and repeatable behavior, cannot fully secure non-deterministic systems, where the same input may not always produce the same outcome.

AI agents can interpret the same instruction differently, chain individually authorized actions into an unsafe outcome, or pursue a legitimate goal through a method the organization did not anticipate. One of the most recent examples of this is the incident that OpenAI and Hugging Face jointly disclosed, where an autonomous agent escaped its intended testing boundaries and compromised Hugging Face infrastructure.  

An agent may have permission to access data and invoke a tool, but that does not mean every use of that access is appropriate. It is not enough to know whether an action is allowed. Organizations need to know whether it makes sense.

  • Is this normal for this agent?  
  • Is it acting within its intended purpose?  
  • Is it accessing unusual data, invoking an unexpected tool, or beginning to drift?  
  • Do a series of ordinary-looking actions become risky when viewed together?

Behavioral-based detection specific to an environment or organization with an understanding of context and risk enables provides the needed detection engineering for AI systems. It learns normal activity across people, systems, data, devices, and AI agents, then identifies deviations and evaluates their risk, intent, and context. This enables detection of misuse, abuse, compromise, manipulation, and unintended behavior even when no known attack signature or explicit policy violation exists.

Why accuracy is the foundation for SOC optimization

AI will only improve the SOC if it produces accurate, explainable, and actionable outcomes.

If analysts must manually validate every AI-generated finding because they cannot understand the evidence or confidence behind it, automation has not reduced workload. It has moved the workload. False positives increase fatigue. False negatives cause the most risk and damage to organizations. Inaccurate autonomous actions can disrupt critical operations.

Accuracy is therefore more than a model-performance metric. It is the prerequisite for analyst trust, SOC optimization, and safe autonomous response.

That accuracy is unlikely to come from one model.

Generative AI is valuable for natural-language analysis, summarization, and human interaction. But an LLM should not be the sole analytical engine for behavioral-based detection, investigation, risk assessment, and containment. Interpretability and consistency are required for high-consequence security decisions.

A stronger architecture uses multiple specialized AI systems collaboratively:  

  • Behavioral models can establish normal activity.  
  • Unsupervised learning can identify novel anomalies.  
  • Graph analysis can evaluate relationships among agents, identities, systems, and tools.  
  • Other models can correlate events, investigate competing hypotheses, and assess risk.  
  • Semantic models can analyze language where behavior-based language analysis is needed but this can be used in tandem with vector embeddings, graph neural networks, and a variety of other AI systems.

Each model contributes a different analytical perspective. Their outputs can corroborate one another, improving accuracy and creating a more reliable basis for response. The objective is not one model operating as an oracle. It is layered, adaptive intelligence designed to produce decisions the SOC can understand and trust.

Autonomous containment is required to secure autonomous systems

Many SOCs remain hesitant to trust LLM-based agents with autonomous containment. That concern is reasonable. A poorly selected response can isolate the wrong asset, stop a critical workflow, block a legitimate identity, or create more operational damage than the original incident.

But relying exclusively on human response is also not viable.

AI systems can operate at machine speed. They can expose sensitive data, execute workflows, modify records, call tools, or propagate actions across connected systems before an analyst can investigate and intervene. The behavior may be unintentional, the result of an agent optimizing toward a goal, or caused by misuse, compromise, prompt injection, or offensive AI.

Intent affects the investigation. It does not change the need to stop the damage.

Organizations need autonomous response, but it must be surgical and explainable. The objective is not to shut down an entire agent, user, application, or business process whenever an anomaly occurs. It is to interrupt the specific risky behavior: block an unusual connection, constrain a tool call, stop an abnormal data transfer, or temporarily limit an agent when it is performing anomalous, risky activity.  

That buys humans time. It stops the spread, limits damage, and allows the SOC to investigate without unnecessarily disrupting the business.

Layered, Adaptive AI provides a path forward

Darktrace has spent more than a decade researching and operationalizing layered, behavioral, Adaptive AI that learns a specific organization rather than relying only on historic attacks or predefined signatures.

The approach is designed to understand normal behavior, identify anomalous activity, assess its risk, correlate related events, autonomously investigate, and, when necessary, apply targeted containment while normal operations continue.

That sequence matters. Autonomous response cannot simply be added to the end of an LLM workflow. Trusted containment depends on broad visibility, continuous behavioral understanding, multiple analytical techniques, risk and context evaluation, autonomous investigation, explainability, and precise response actions.

This represents a more responsible model for security autonomy: not automation for its own sake, but controlled autonomy built to improve security outcomes and protect business operations.

Security must enable AI adoption

The answer for security teams is not to block AI. Organizations are adopting it to improve productivity, accelerate development, and create new business value.

But innovation without behavioral detection and autonomous containment is not sustainable.

Organizations should continue investing in governance, identity, least privilege, data security, secure MLOps, guardrails, testing, evaluation, validation, verification, kill switches, rollback, and forensic readiness. At the same time, they cannot wait for every governance program to mature before addressing runtime risk.

Behavioral-based detection and autonomous containment provide an immediate layer of resilience. They allow organizations to detect exploitation and risky AI behavior they did not anticipate, contain it at machine speed, and preserve human control over broader remediation.

The future of AI security will not be defined by a single model making every decision. It will be defined by multiple specialized AI systems working collaboratively, with sufficient accuracy, transparency, and context to support trusted autonomous action.

Surgical, explainable autonomous containment is no longer a future capability. It is a requirement for scaling AI securely today.

Learn how to build a defense-in-depth strategy for securing AI at scale in our talk at Black Hat on August 5 at 3:15 PM.  

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