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June 25, 2024

Let the Dominos Fall! SOC and IR Metrics for ROI

Vendors are scrambling to compare MTTD metrics laid out in the latest MITRE Engenuity ATT&CK® Evaluations. But this analysis is reductive, ignoring the fact that in cybersecurity, there are far more metrics that matter.
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
John Bradshaw
Sr. Director, Technical Marketing
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25
Jun 2024

One of the most enjoyable discussions (and debates) I engage in is the topic of Security Operations Center (SOC) and Incident Response (IR) metrics to measure and validate an organization’s Return on Investment (ROI). The debate part comes in when I hear vendor experts talking about “the only” SOC metrics that matter, and only list the two most well-known, while completely ignoring metrics that have a direct causal relationship.

In this blog, I will discuss what I believe are the SOC/IR metrics that matter, how each one has a direct impact on the others, and why organizations should ensure they are working towards the goal of why these metrics are measured in the first place: Reduction of Risk and Costs.

Reduction of Risk and Costs

Every security solution and process an organization puts in place should reduce the organization’s risk of a breach, exposure by an insider threat, or loss of productivity. How an organization realizes net benefits can be in several ways:

  • Improved efficiencies can result in SOC/IR staff focusing on other areas such as advanced threat hunting rather than churning through alerts on their security consoles. It may also help organizations dealing with the lack of skilled security staff by using Artificial Intelligence (AI) and automated processes.
  • A well-oiled SOC/IR team that has greatly reduced or even eliminated mundane tasks attracts, motivates, and retains talent resulting in reduced hiring and training costs.
  • The direct impact of a breach such as a ransomware attack can be devastating. According to the 2024 Data Breach Investigations Report by Verizon, MGM Resorts International reported the ALPHV ransomware cost the company approximately $100 million[1].
  • Failure to take appropriate steps to protect the organization can result in regulatory fines; and if an organization has, or is considering, purchasing Cyber Insurance, can result in declined coverage or increased premiums.

How does an organization demonstrate they are taking proactive measures to prevent breaches? That is where it's important to understand the nine (yes, nine) key metrics, and how each one directly influences the others, play their roles.

Metrics in the Incident Response Timeline

Let’s start with a review of the key steps in the Incident Response Timeline:

Seven of the nine key metrics are in the IR timeline, while two of the metrics occur before you ever have an incident. They occur in the Pre-Detection Stage.

Pre-Detection stage metrics are:

  • Preventions Per Intrusion Attempt (PPIA)
  • False Positive Reduction Rate (FPRR)

Next is the Detect and Investigate stage, there are three metrics to consider:

  • Mean Time to Detection (MTTD)
  • Mean Time to Triage (MTTT)
  • Mean Time to Understanding (MTTU)

This is followed by the Remediation stage, there are two metrics here:

  • Mean Time to Containment (MTTC)
  • Mean Time to Remediation / Recovery (MTTR)

Finally, there is the Risk Reduction stage, there are two metrics:

  • Mean Time to Advice (MTTA)
  • Mean Time to Implementation (MTTI)

Pre-Detection Stage

Preventions Per Intrusion Attempt

PPIA is defined as stopping any intrusion attempt at the earliest possible stage. Your network Intrusion Prevention System (IPS) blocks vulnerability exploits, your e-mail security solution intercepts and removes messages with malicious attachments or links, your egress firewall blocks unauthorized login attempts, etc. The adversary doesn’t get beyond Step 1 in the attack life cycle.

This metric is the first domino. Every organization should strive to improve on this metric every day. Why? For every intrusion attempt you stop right out of the gate, you eliminate the actions for every other metric. There is no incident to detect, triage, investigate, remediate, or analyze post-incident for ways to improve your security posture.

When I think about PPIA, I always remember back to a discussion with a former mentor, Tim Crothers, who discussed the benefits of focusing on Prevention Failure Detection.

The concept is that as you layer your security defenses, your PPIA moves ever closer to 100% (no one has ever reached 100%). This narrows the field of fire for adversaries to breach into your organization. This is where novel, unknown, and permuted threats live and breathe. This is where solutions utilizing Unsupervised Machine Learning excel in raising anomalous alerts – indications of potential compromise involving one of these threats. Unsupervised ML also raises alerts on anomalous activity generated by known threats and can raise detections before many signature-based solutions. Most organizations struggle to find strong permutations of known threats, insider threats, supply chain attacks, attacks utilizing n-day and 0-day exploits. Moving PPIA ever closer to 100% also frees your team up for conducting threat hunting activities – utilizing components of your SOC that collect and store telemetry to query for potential compromises based on hypothesis the team raises. It also significantly reduces the alerts your team must triage and investigate – solving many of the issues outlined at the start of this paper.

False Positive Reduction Rate

Before we discuss FPRR, I should clarify how I define False Positives (FPs). Many define FPs as an alert that is in error (i.e.: your EDR alerts on malware that turns out to be AV signature files). While that is a FP, I extend the definition to include any alert that did not require triage / investigation and distracts the SOC/IR team (meaning they conducted some level of triage / investigation).

This metric is the second domino. Why is this metric important? Every alert your team exerts time and effort on that is a non-issue distracts them from alerts that matter. One of the major issues that has resonated in the security industry for decades is that SOCs are inundated with alerts and cannot clear the backlog. When it comes to PPIA + FPRR, I have seen analysts spend time investigating alerts that were blocked out of the gate while their screen continued to fill up with more. You must focus on Prevention Failure Detection to get ahead of the backlog.

Detect and Investigate Stages

Mean Time to Detection

MTTD, or “Dwell Time”, has decreased dramatically over the past 12 years. From well over a year to 16 days in 2023[2]. MTTD is measured from the earliest possible point you could detect the intrusion to the moment you actually detect it.

This third domino is important because the longer an adversary remains undetected, the more the odds increase they will complete their mission objective. It also makes the tasks of triage and investigation more difficult as analysts must piece together more activity and adversaries may be erasing evidence along the way – or your storage retention does not cover the breach timeline.

Many solutions focusing solely on MTTD can actually create the very problem SOCs are looking to solve.  That is, they generate so much alerting that they flood the console, email, or text messaging app causing an unmanageable queue of alerts (this is the problem XDR solutions were designed to resolve by focusing on incidents rather than alerts).

Mean Time to Triage

MTTT involves SOCs that utilize Level 1 (aka Triage) analysts to render an “escalate / do not escalate” alert verdict accurately. Accuracy is important because Triage Analysts typically are staff new to cyber security (recent grad / certification) and may over escalate (afraid to miss something important) or under escalate (not recognize signs of a successful breach). Because of this, a small MTTT does not always equate to successful handling of incidents.

This metric is important because keeping your senior staff focused on progressing incidents in a timely manner (and not expending time on false positives) should reduce stress and required headcount.

Mean Time to Understanding

MTTU deals with understanding the complete nature of the incident being investigated. This is different than MTTT which only deals with whether the issue merits escalation to senior analysts. It is then up to the senior analysts to determine the scope of the incident, and if you are a follower of my UPSET Investigation Framework, you know understanding the full scope involves:

U = All compromised accounts

P = Persistence Mechanisms used

S = All systems involved (organization, adversary, and intermediaries)

E = Endgame (or mission objective)

T = Techniques, Tactics, Procedures (TTPs) utilized by the adversary

MTTU is important because this information is critical before any containment or remediation actions are taken. Leave a stone unturned, and you alert the adversary that you are onto them and possibly fail to close an avenue of access.

Remediation Stages

Mean Time to Containment

MTTC deals with neutralizing the threat. You may not have kicked the adversary out, but you have halted their progress to their mission objective and ability to inflict further damage. This may be through use of isolation capabilities, termination of malicious processes, or firewall blocks.

MTTC is important, especially with ransomware attacks where every second counts. Faster containment responses can result in reduced / eliminated disruption to business operations or loss of data.

Mean Time to Remediation / Recovery

The full scope of the incident is understood, the adversary has been halted in their tracks, no malicious processes are running on any systems in your organization. Now is the time to put things back to right. MTTR deals with the time involved in restoring business operations to pre-incident stage. It means all remnants of changes made by the adversary (persistence, account alterations, programs installed, etc.) are removed; all disrupted systems are restored to operations (i.e.: ransomware encrypted systems are recovered from backups / snapshots), compromised user accounts are reset, etc.

MTTR is important because it informs senior management of how fast the organization can recover from an incident. Disaster Recovery and Business Continuity plans play a major role in improving this score.

Risk Reduction Stages

Mean Time to Advice

After the dust has settled from the incident, the job is not done. MTTA deals with identifying and assessing the specific areas (vulnerabilities, misconfigurations, lack of security controls) that permitted the adversary to advance to the point where detection occurred (and any actions beyond). The SOC and IR teams should then compile a list of recommendations to present to management to improve the security posture of the organization so the same attack path cannot be used.

Mean Time to Implement

Once recommendations are delivered to management, how long does it take to implement them? MTTI tracks this timeline because none of it matters if you don’t fix the holes that led to the breach.

Nine Dominos

There are the nine dominos of SOC / IR metrics I recommend helping organizations know if they are on the right track to reduce risk, costs and improve morale / retention of the security teams. You may not wish to track all nine, but understanding how each metric impacts the others can provide visibility into why you are not seeing expected improvements when you implement a new security solution or change processes.

Improving prevention and reducing false positives can make huge positive impacts on your incident response timeline. Utilizing solutions that get you to resolution quicker allows the team to focus on recommendations and risk reduction strategies.

Whichever metrics you choose to track, just be sure the dominos fall in your favor.

References

[1] 2024 Verizon Data Breach Investigations Report, p83

[2] Mandiant M-Trends 2023

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
John Bradshaw
Sr. Director, Technical Marketing

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July 27, 2026

Hiding in Plain Sight: Uncovering a Multi-Stage Ransomware Attack Through Behavioral Detection

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Why ransomware has changed

Ransomware attacks have continued to increase globally, with 698 incidents reported in May 2026, representing a 48% rise compared to 472 incidents in May 2025 [1]. At the same time, the ransomware landscape is evolving. Several major ransomware groups, including LockBit [2], have been disrupted by successful joint law enforcement operations, resulting in a shift away from a small number of dominant actors towards a more fragmented and distributed ecosystem. This is increasingly composed of smaller groups who play a specialized role in the attack, such as initial access brokers, affiliates or developers.

As a result, ransomware tactics, techniques, and procedures (TTPs) are becoming more diverse and less predictable. On top of this, adversaries are leveraging native tools and legitimate penetration testing frameworks to evade detection. Anomaly-based detection is therefore critical to identify pre-ransomware activity, rather than relying on signatures associated with a handful of well-known ransomware groups.

As these attacks often unfold over several days, there is a critical window for defenders to act. In this context, behavioral-based detection plays a vital role in identifying suspicious pre-ransomware activity, and enabling early intervention before encryption or exfiltration occurs.

Inside a modern ransomware intrusion

In early 2026, Darktrace detected activity within a customer’s environment related to a multi-stage ransomware intrusion from the initial compromise. This activity does not appear to be attributable to a specific ransomware group, and no known ransomware payload was observed until the final stage.

The attack aligns with a broader industry trend in which compromised virtual private network (VPN) credentials are used as an entry point, followed by rapid internal reconnaissance and lateral movement using legitimate administrative tools. This growing preference for native tools and legitimate frameworks in cyber-attacks illustrates that it is increasingly unreliable to depend solely on traditional indicators of compromise such as known malware signatures or exploit detection.

The intrusion also involved the use of Sliver, an open-source adversary emulation framework, which is increasingly observed in real-world attacks. Originally designed for penetration testing and red teaming, Sliver has gained traction among threat actors as a stealthier alternative to more heavily signatured frameworks such as Cobalt Strike. As a legitimate framework, its use further complicates detection for security tools that rely on known malicious signatures.

Darktrace’s detection of a ransomware event in a customer’s environment

The initial compromise appears to have occurred via compromised credentials used over the VPN shortly before, or at the onset of the first indicators of suspicious activity. While it remains unclear as to how or when the threat actors gained access to these credentials, the use of initial access brokers (IABs) is a common feature of modern ransomware operations. This suggests that access to the environment may have been established several days or weeks beforehand.

The intrusion unfolded over three days, presenting multiple opportunities for early detection and intervention before ransomware deployment. The attack progressed through a compressed but structured sequence: initial access and reconnaissance were completed within hours, followed by privilege escalation and lateral movement the next day, and culminating in data exfiltration and encryption shortly thereafter. Throughout each stage, distinct behavioral anomalies emerged across the network providing clear indicators of malicious activity well before the ransomware was deployed.

While Darktrace’s Autonomous Response capability was enabled within the customer’s environment, it was not fully configured across the impacted devices, allowing the attack to progress to ransomware deployment. Had Autonomous Response been fully deployed across the affected systems, it could have taken targeted action against the earliest stages of malicious activity, potentially disrupting the intrusion before it escalated.

Figure 1: Timeline of the attack progression.

Day 1: Reconnaissance and privilege escalation

The threat actor gained access via compromised VPN credentials and initiated internal reconnaissance. Darktrace detected anomalous scanning behavior, including unusual port scanning activity and widespread network enumeration.

Specifically, Darktrace detected a high volume of east-west scanning activity across a broad range of ports, with TCP connections targeting ports 21, 80, 445, 4899 and 8080. Associated URIs suggested the use of Nmap, a widely used penetration testing tool. This highlights how attackers often leverage legitimate penetration testing tools for malicious reconnaissance, enabling them to blend into normal network activity and evade traditional signature-based detection methods.

Figure 2: Darktrace's detection of a sharp increase in anomalous internal connections, triggering multiple high-severity model alerts associated with reconnaissance activity.

Several devices were observed using administrative credentials to carry out privileged actions in a manner that was highly anomalous for the environment. This activity was accompanied by behavior consistent with SMB authentication scanning, suggesting efforts to identify and access additional systems. As the activity intensified, an increasing number of devices became involved, signalling lateral movement and further spread across the network.

Darktrace also identified privilege escalation through active directory (AD) replication abuse, specifically via the drsuapi::DRSGetNCChanges function. This technique allows an attacker with sufficient privileges to request directory replication data from a domain controller (DC), enabling them to extract credentials, including password hashes, without directly interacting with user accounts. Commonly associated with ‘DCSync’ attacks, this technique is frequently used to obtain highly privileged credentials and enable further escalation within an environment.

Figure 3: Darktrace’s detection of anomalous AD replication activity indicative of privilege escalation.

This activity was seen alongside the use of the now obsolete SMBv1, repeated NTLM authentication attempts using multiple variations of ‘Administrator’ credentials, reverse DNS scanning, and large-scale network scanning. Darktrace observed widespread use of SMBv1 across the customer’s environment, exposing a significant security weakness. As a legacy protocol with well-documented weaknesses, SMBv1 can be exploited to facilitate lateral movement, allowing the attackers to expand their access following initial compromise.

Day 3: Lateral Movement, Command & Control, and Exfiltration

Two days later, the attacker escalated privileges and expanded their foothold using living-off-the-land (LOTL) techniques such as PSExec, WMI, and RDP. Concurrently, Darktrace identified command-and-control (C2)-style communications consistent with the Sliver framework, alongside rare outbound connections to cloud infrastructure indicating potential data exfiltration. The volume and severity of observed activity increased as attack behavior intensified.

The device was observed conducting extensive lateral movement, leveraging LOTL techniques to evade detection. Activity included WMI execution (e.g. ExecQuery), DCE-RPC activity, SMB sessions and file writes, most of which were successful, as well as the deployment of PSEXESVC.exe via ADMIN$ shares and prolonged RDP sessions. Darktrace identified this behavior as highly anomalous for the environment. Such activity is commonly associated with the transfer of attacker tooling, remote command execution, and the establishment of persistent access across compromised systems.  

Figure 4: Darktrace’s detection of a spike in RPC binding events indicative of potential lateral movement.

On the same day, Darktrace detected C2-style SSL communications originating from multiple internal devices to rare external endpoints. These connections exhibited anomalous characteristics, including invalid SSL certificates and repeated connection patterns resembling beaconing. Analysis of the observed JA3 fingerprint further linked the activity to Sliver, the adversary simulation framework referenced earlier, as the hash has previously been associated with Sliver-related infrastructure [3]. The use of this framework reflects a broader trend of attackers repurposing legitimate offensive security tools for stealthy C2 communications. Connections to 137[.]220[.]59[.]55 (ASN AS20473 AS-VULTR) indicated that the communications were likely routed via a virtual private server (VPS) hosted by Vultr. Attackers often utilize VPS infrastructure from legitimate cloud providers like Vultr to obscure their true origin, blend into benign traffic, and evade IP-based detection mechanisms [4].

Figure 5: Darktrace’s Cyber AI Analyst detection of two linked unusual connections to Vultr infrastructure.

Darktrace also observed a device initiating SSL connections to safedata.s3[.]wasabisys[.]com, an endpoint associated with Wasabi cloud storage. Darktrace recognized that neither the destination nor the associated IP address had previously been observed within the environment.  More than 200 MB of data was subsequently uploaded externally to endpoints sharing the same JA3 client hash, indicating a sustained transfer session and potential data exfiltration to third-party storage. The apparent exfiltration prior to encryption is consistent with a double-extortion ransomware strategy.

Figure 6: Darktrace’s Cyber AI Analyst detection of more than 30 rare outbound connections to a Wasabi cloud storage endpoint, indicative of potential data exfiltration

Day 4: Encryption

The attack culminated in ransomware deployment, marking the transition from suspicious network activity to a business-impacting incident. Using SMB-based propagation, the threat actor encrypted thousands of files across the network, affecting multiple systems and disrupting normal operations. Throughout the encryption event, the legacy SMBv1 protocol was used extensively across multiple internal systems, resulting in a significant increase in newly encrypted files.

Figure 7: Darktrace’s detection of abnormal spikes in SMB activity and associated model alerts, indicative of ransomware encryption and propagation.

Darktrace’s Cyber AI Analyst automatically investigated and correlated the encryption activity and related events into a single incident narrative, providing the customer with real-time visibility into the attack while significantly reducing investigation time.

Figure 8: Darktrace’s Cyber AI Analyst’s investigation into the encryption activity. AI Analyst incident detailing example encryption activity in real time. Related events are automatically correlated and summarized into a clear narrative, reducing investigation time.

Defender action recommendations

What Could Have Stopped the Attack Earlier?

Although the attack ultimately resulted in ransomware deployment, there were multiple opportunities to detect, contain, and disrupt the intrusion before encryption occurred. The following actions could have significantly reduced the overall impact:

Detect and investigate indicators of reconnaissance and lateral movement

  • Unusual scanning
  • Active Directory replication anomalies consistent with DCSync activity
  • Anomalous use of native tools and processes indicative of LOTL attacks
  • Unusual use of common reconnaissance tools such as Nmap and NetScan

Contain compromised credentials and affected devices

  • Disable and reset compromised VPN credentials
  • Isolate devices performing anomalous scanning and lateral movement activity

Block suspicious external communications and data exfiltration

  • Use anomaly-based detection to detect and block repeated outbound connections to rare external infrastructure
  • Prevent data exfiltration to unauthorized cloud storage services such as Wasabi

Conclusion

The incident highlights the importance of anomaly-based detection, particularly against attacks that primarily use native or legitimate tools to evade traditional security measures. Darktrace identified suspicious activity from the first day of the compromise, providing multiple opportunities to disrupt the intrusion before it progressed to lateral movement and data exfiltration.

In this instance, detection was not the limiting factor; response time was. Prompt investigation and containment of devices exhibiting anomalous behavior could have prevented lateral movement, data exfiltration, and ultimately ransomware deployment.

As adversaries increasingly prioritize stealth over custom malware, relying instead on legitimate tools, valid credentials, and trusted infrastructure, traditional signature-based detection becomes less effective. Identifying subtle behavioral deviations early remains critical to disrupting attacks before they escalate into full-scale ransomware incidents.

Credit to Alexandra Evzona (Cyber Analyst), Priya Thapa (Senior Cyber Analyst)
Edited by Ryan Traill (Content Manager)

Appendices

References

[1] https://industrialcyber.co/ransomware/check-point-reports-ransomware-attacks-jump-48-year-over-year-despite-decline-in-overall-cyberattack-activity/

[2] https://www.europol.europa.eu/media-press/newsroom/news/law-enforcement-disrupt-worlds-biggest-ransomware-operation

[3] https://fieldeffect.com/blog/field-effect-mitigates-not-so-simplehelp-exploits-enabling-deployment-of-backdoors

[4] https://www.darktrace.com/blog/from-vps-to-phishing-how-darktrace-uncovered-saas-hijacks-through-virtual-infrastructure-abuse

Indicators of Compromise (IoCs)

IP Addresses

  • 137[.]220[.]59[.]55 – Potential C2 infrastructure (Vultr AS20473)
  • 38[.]27[.]106[.]123 – Wasabi cloud storage endpoint associated with potential data exfiltration
  • 38[.]27[.]106[.]128 – Wasabi cloud storage endpoint associated with potential data exfiltration
  • 38[.]27[.]106[.]117 – Wasabi cloud storage endpoint associated with potential data exfiltration

Domains

  • safedata[.]s3[.]wasabisys[.]com – Potential data exfiltration endpoint
  • *.wasabisys[.]com – Associated Wasabi cloud storage infrastructure

JA3 Fingerprint

  • d6828e30ab66774a91a96ae93be4ae4c – Associated with the Sliver adversary emulation framework

Files

  • Delete[.]me – File observed during reconnaissance activity, commonly associated with NetScan

Darktrace Model Coverage

·       Anomalous Connection / Active Remote Desktop Tunnel

·       Anomalous Connection / Anomalous Remote Registry Service Control

·       Anomalous Connection / Multiple Failed Windows UDP

·       Anomalous Connection / New or Uncommon Service Control

·       Anomalous Connection / New or Uncommon Service Enumeration

·       Anomalous Connection / New User Agent to IP Without Hostname

·       Anomalous Connection / SMB Enumeration

·       Anomalous Connection / Suspicious Activity On High Risk Device

·       Anomalous Connection / Suspicious Read Write Ratio

·       Anomalous Connection / Sustained MIME Type Conversion

·       Anomalous Connection / Uncommon 1 GiB Outbound

·       Anomalous Connection / Unusual Admin RDP Session

·       Anomalous Connection / Unusual Admin SMB Session

·       Anomalous Connection / Unusual SMB Version 1 Connectivity

·       Anomalous File / EXE from Rare External Location

·       Anomalous File / Internal / Additional Extension Appended to SMB File

·       Anomalous Server Activity / Outgoing from Server

·       Compromise / Beaconing Activity To External Rare

·       Compromise / Ransomware / Possible Ransom Note Read

·       Compromise / Ransomware / Ransom or Offensive Words Written to SMB

·       Compromise / Ransomware / Suspicious SMB Activity

·       Device / Anonymous NTLM Logins

·       Device / Attack and Recon Tools

·       Device / Initial Attack Chain Activity

·       Device / Large Number of Model Alerts

·       Device / Long Agent Connection to New Endpoint

·       Device / Multiple Lateral Movement Model Alerts

·       Device / Network Scan

·       Device / New or Uncommon SMB Named Pipe

·       Device / New or Unusual Remote Command Execution

·       Device / New User Agent

·       Device / Possible RPC Lateral Movement

·       Device / Possible SMB/NTLM Brute Force

·       Device / RDP Scan

·       Device / SMB Lateral Movement

·       Device / SMB Session Brute Force (Non-Admin)

·       Device / SMB Version 1 Access Failures

·       Device / Suspicious File Delete Activity

·       Device / Suspicious Network Scan Activity

·       Device / Unusual SMB Error Detected

·       Device / Unusual SMB To Critical Resource

·       Device / Unusual Winreg Operation

·       Unusual Activity / Multiple Failed Internal Connections

·       Unusual Activity / Possible RPC Recon Activity

·       Unusual Activity / Sustained Anomalous SMB Activity

·       Unusual Activity / Unusual External Data to New Endpoint

·       Unusual Activity / Unusual External Data Transfer

·       Unusual Activity / Unusual File Storage Data Transfer

·       Unusual Activity / Unusual Large Internal Transfer

·       User / New Admin Credentials on Client

·       User / NTLM Login from Unauthenticated Device

Autonomous Response Model Alerts

·       Antigena / Network / External Threat / Antigena Ransomware Block

·       Antigena / Network / External Threat / Antigena Suspicious File Block

·       Antigena / Network / Insider Threat / Antigena Active Threat SMB Write Block

·       Antigena / Network / Insider Threat / Antigena Internal Anomalous File Activity

·       Antigena / Network / Insider Threat / Antigena Large Data Volume Outbound Block

·       Antigena / Network / Insider Threat / Antigena Network Scan Block

·       Antigena / Network / Insider Threat / Antigena Unusual Privileged User Activities Block

·       Antigena / Network / Manual / Quarantine Device

·       Antigena / Network / Significant Anomaly / Antigena Alerts Over Time Block

·       Antigena / Network / Significant Anomaly / Antigena Controlled and Model Alert

·       Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Client Block

·       Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block 100

·       Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

MITRE ATT&CK MAPPING

Command and control - Protocol Tunneling - T1572

Command and control – Web Protocols – T1071.001

Credential Access- Exploitation for Credential Access- T1212

Credential Access- Password Guessing- T1110

Discovery- File and Directory Discovery- T1083

Discovery- Network Service Discovery- T1046

Discovery- Network Share Discovery- T1135

Discovery- Remote System Discovery- T1018

Exfiltration- Exfiltration Over C2 Channel- T1041

Exfiltration- Exfiltration to Cloud Storage- T1567

Impact- Data Encrypted for Impact- T1486

Impact- Data Encrypted for Impact- T1486

Impact- Service Stop - T1489

Initial Access- Public-Facing Application- T1190

Lateral Movement- Exploitation of Remote Services- T1210

Lateral Movement- Remote Desktop Protocol- T1021

Lateral Movement- SMB/Windows Admin Shares- T1021

Lateral Movement- Taint Shared Content- T1080

Persistence- Modify Registry- T1112

Privilege Escalation- Exploitation for Privilege Escalation- T0890

Privilege Escalation- Valid Accounts- T1078

Reconnaissance- Scanning IP Blocks- T1595

Reconnaissance- Vulnerability Scanning- T1595

Resource Development- Malware- T1588

Stealth- File Deletion- T1070

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Alexandra Evzona
Cyber Analyst

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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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About the author
Carlos Gray
Senior Product Marketing Manager, Email
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