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.

Written by

John Bradshaw

Sr. Director, Technical Marketing

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

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

‍

Written by

John Bradshaw

Sr. Director, Technical Marketing

Inside the SOC

Written by

John Bradshaw

Sr. Director, Technical Marketing

Associate Principal Analyst & Regional Consultancy Lead

•

March 26, 2026

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March 26, 2026

Phantom Footprints: Tracking GhostSocks Malware

Why are attackers using residential proxies?

In today's threat landscape, blending in to normal activity is the key to success for attackers and the growing reliance on residential proxies shows a significant shift in how threat actors are attempting to bypass IP detection tools.

The increasing dependency on residential proxies has exposed how prevalent proxy services are and how reliant a diverse range of threat actors are on them. From cybercriminal groups to state‑sponsored actors, the need to bypass IP detection tools is fundamental to the success of these groups. One malware that has quietly become notorious for its ability to avoid anomaly detection is GhostSocks, a malware that turns compromised devices into residential proxies.

What is GhostSocks?

Originally marketed on the Russian underground forum xss[.]is as a Malware‑as‑a‑Service (MaaS), GhostSocks enables threat actors to turn compromised devices into residential proxies, leveraging the victim's internet bandwidth to route malicious traffic through it.

How does Ghostsocks malware work?

The malware offers the threat actor a “clean” IP address, making it look like it is coming from a household user. This enables the bypassing of geographic restrictions and IP detection tools, a perfect tool for avoiding anomaly detection. It wasn’t until 2024, when a partnership was announced with the infamous information stealer Lumma Stealer, that GhostSocks surged into widespread adoption and alluded to who may be the author of the proxy malware.

Written in GoLang, GhostSocks utilizes the SOCKS5 proxy protocol, creating a SOCKS5 connection on infected devices. It uses a relay‑based C2 implementation, where an intermediary server sits in between the real command-and-control (C2) server and the infected device.

How does Ghostsocks malware evade detection?

To further increase evasion, the Ghostsocks malware wraps its SOCKS5 tunnels in TLS encryption, allowing its malicious traffic to blend into normal network traffic.

Early variants of GhostSocks do not implement a persistence mechanism; however, later versions achieve persistence via registry run keys, ensuring sustained proxy operational time [1].

While proxying is its primary purpose, GhostSocks also incorporates backdoor functionality, enabling malicious actors to run arbitrary commands and download and deploy additional malicious payloads. This was evident with the well‑known ransomware group Black Basta, which reportedly used GhostSocks as a way of maintaining long‑term access to victims’ networks [1].

Darktrace’s detection of GhostSocks Malware

Darktrace observed a steady increase in GhostSocks activity across its customer base from late 2025, with its Threat Research team identifying multiple incidents involving the malware. In one notable case from December 2025, Darktrace detected GhostSocks operating alongside Lumma Stealer, reinforcing that the partnership between Lumma and GhostSocks remains active despite recent attempts to disrupt Lumma’s infrastructure.

Darktrace’s first detection of GhostSocks‑related activity came when a device on the network of a customer in the education sector began making connections to an endpoint with a suspicious self‑signed certificate that had never been seen on the network before.

The endpoint in question, 159.89.46[.]92 with the hostname retreaw[.]click, has been flagged by multiple open‑source intelligence (OSINT) sources as being associated with Lumma Stealer’s C2 infrastructure [2], indicating its likely role in the delivery of malicious payloads.

Figure 1: Darktrace’s detection of suspicious SSL connections to retreaw[.]click, indicating an attempted link to Lumma C2 infrastructure.

Less than two minutes later, Darktrace observed the same device downloading the executable (.exe) file “Renewable.exe” from the IP 86.54.24[.]29, which Darktrace recognized as 100% rare for this network.

Figure 2: Darktrace’s detection of a device downloading the unusual executable file “Renewable.exe”.

Both the file MD5 hash and the executable itself have been identified by multiple OSINT vendors as being associated with the GhostSocks malware [3], with the executable likely the backdoor component of the GhostSocks malware, facilitating the distribution of additional malicious payloads [4].

Following this detection, Darktrace’s Autonomous Response capability recommended a blocking action for the device in an early attempt to stop the malicious file download. In this instance, Darktrace was configured in Human Confirmation Mode, meaning the customer’s security team was required to manually apply any mitigative response actions. Had Autonomous Response been fully enabled at the time of the attack, the connections to 86.54.24[.]29 would have been blocked, rendering the malware ineffective at reaching its C2 infrastructure and halting any further malicious communication.

Figure 3: Darktrace’s Autonomous Response capability suggesting blocking the suspicious connections to the unusual endpoint from which the malicious executable was downloaded.

As the attack was able to progress, two days later the device was detected downloading additional payloads from the endpoint www.lbfs[.]site (23.106.58[.]48), including “Setup.exe”, “,.exe”, and “/vp6c63yoz.exe”.

Figure 4: Darktrace’s detection of a malicious payload being downloaded from the endpoint www.lbfs[.]site.

Once again, Darktrace recognized the anomalous nature of these downloads and suggested that a “group pattern of life” be enforced on the offending device in an attempt to contain the activity. By enforcing a pattern of life on a device, Darktrace restricts its activity to connections and behaviors similar to those performed by peer devices within the same group, while still allowing it to carry out its expected activity, effectively preventing deviations indicative of compromise while minimizing disruption. As mentioned earlier, these mitigative actions required manual implementation, so the activity was able to continue. Darktrace proceeded to suggest further actions to contain subsequent malicious downloads, including an attempt to block all outbound traffic to stop the attack from progressing.

Figure 5: An overview of download activity and the Autonomous Response actions recommended by Darktrace to block the downloads.

Around the same time, a third executable download was detected, this time from the hostname hxxp[://]d2ihv8ymzp14lr.cloudfront.net/2021-08-19/udppump[.]exe, along with the file “udppump.exe”.While GhostSocks may have been present only to facilitate the delivery of additional payloads, there is no indication that these CloudFront endpoints or files are functionally linked to GhostSocks. Rather, the evidence points to broader malicious file‑download activity.

Shortly after the multiple executable files had been downloaded, Darktrace observed the device initiating a series of repeated successful connections to several rare external endpoints, behavior consistent with early-stage C2 beaconing activity.

Cyber AI Analyst’s investigation

Figure 7: Darktrace’s detection of additional malicious file downloads from malicious CloudFront endpoints.

Throughout the course of this attack, Darktrace’s Cyber AI Analyst carried out its own autonomous investigation, piecing together seemingly separate events into one wider incident encompassing the first suspicious downloads beginning on December 4, the unusual connectivity to many suspicious IPs that followed, and the successful beaconing activity observed two days later. By analyzing these events in real-time and viewing them as part of the bigger picture, Cyber AI Analyst was able to construct an in‑depth breakdown of the attack to aid the customer’s investigation and remediation efforts.

Figure 8: Cyber AI Analyst investigation detailing the sequence of events on the compromised device, highlighting its extensive connectivity to rare endpoints, the related malicious file‑download activity, and finally the emergence of C2 beaconing behavior.

Conclusion

The versatility offered by GhostSocks is far from new, but its ability to convert compromised devices into residential proxy nodes, while enabling long‑term, covert network access—illustrates how threat actors continue to maximise the value of their victims’ infrastructure. Its growing popularity, coupled with its ongoing partnership with Lumma, demonstrates that infrastructure takedowns alone are insufficient; as long as threat actors remain committed to maintaining anonymity and can rapidly rebuild their ecosystems, related malware activity is likely to persist in some form.

Credit to Isabel Evans (Cyber Analyst), Gernice Lee (Associate Principal Analyst & Regional Consultancy Lead – APJ)
Edited by Ryan Traill (Content Manager)

Appendices

References

1. https://bloo.io/research/malware/ghostsocks

2. https://www.virustotal.com/gui/domain/retreaw.click/community

3. https://synthient.com/blog/ghostsocks-from-initial-access-to-residential-proxy

4. https://www.joesandbox.com/analysis/1810568/0/html

5. https://www.virustotal.com/gui/url/fab6525bf6e77249b74736cb74501a9491109dc7950688b3ae898354eb920413

Darktrace Model Detections

Real-time Detection Models

Anomalous Connection / Suspicious Self-Signed SSL

Anomalous Connection / Rare External SSL Self-Signed

Anomalous File / EXE from Rare External Location

Anomalous File / Multiple EXE from Rare External Locations

Compromise / Possible Fast Flux C2 Activity

Compromise / Large Number of Suspicious Successful Connections

Compromise / Large Number of Suspicious Failed Connections

Compromise / Sustained SSL or HTTP Increase

Autonomous Response Models

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

Antigena / Network / External Threat / Antigena Suspicious File Block

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

Antigena / Network / External Threat / Antigena File then New Outbound Block

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

Antigena / Network / External Threat / Antigena Suspicious Activity Block

MITRE ATT&CK Mapping

Tactic – Technique – Sub-Technique

Resource Development – T1588 - Malware

Initial Access - T1189 - Drive-by Compromise

Persistence – T1112 – Modify Registry

Command and Control – T1071 – Application Layer Protocol

Command and Control – T1095 – Non-application Layer Protocol

Command and Control – T1071 – Web Protocols

Command and Control – T1571 – Non-Standard Port

Command and Control – T1102 – One-Way Communication

List of Indicators of Compromise (IoCs)

86.54.24[.]29 - IP - Likely GhostSocks C2

http[://]86.54.24[.]29/Renewable[.]exe - Hostname - GhostSocks Distribution Endpoint

http[://]d2ihv8ymzp14lr.cloudfront[.]net/2021-08-19/udppump[.]exe - CDN - Payload Distribution Endpoint

www.lbfs[.]site - Hostname - Likely C2 Endpoint

retreaw[.]click - Hostname - Lumma C2 Endpoint

alltipi[.]com - Hostname - Possible C2 Endpoint

w2.bruggebogeyed[.]site - Hostname - Possible C2 Endpoint

9b90c62299d4bed2e0752e2e1fc777ac50308534 - SHA1 file hash – Likely GhostSocks payload

3d9d7a7905e46a3e39a45405cb010c1baa735f9e - SHA1 file hash - Likely follow-up payload

10f928e00a1ed0181992a1e4771673566a02f4e3 - SHA1 file hash - Likely follow-up payload

About the author

Gernice Lee

Associate Principal Analyst & Regional Consultancy Lead

Blog

AI

March 26, 2026

State of AI Cybersecurity 2026: 92% of security professionals concerned about the impact of AI agents

The findings in this blog are taken from Darktrace's annual State of AI Cybersecurity Report 2026.

AI is already embedded in day-to-day enterprise activity, with 78% of participants in one recent survey reporting that their organizations are using generative AI in at least one business function. Generative AI now acts as an always-on assistant, researcher, creator, and coach across an expanding array of departments and functions. Autonomous agents are performing multi-step operational workflows from end to end. AI features have been layered on top of every SaaS application. And vibe coding is making it possible for employees without deep technical expertise to build their own AI-powered automations.

According to Gartner, more than 80% of enterprises will have deployed GenAI models, applications, or APIs in production environments by the end of this year, up from less than 5% in 2023. Companies report a 130% increase in spending on AI over the same period, with 72% of business leaders using AI tools at least weekly. The outsized efficiency and productivity gains that were once a future vision are quickly becoming everyday reality.

AI is currently driving business growth and innovation, and organizations risk falling behind peers if they don’t keep up with the pace of adoption, but it is also quietly expanding the enterprise attack surface. The modern CISO is challenged to both enable innovation and protect the business from these emerging threats.

AI agents introduce new risks and vulnerabilities

AI agents are playing growing roles in enterprise production environments. In many cases, these agents act with broad permissions across multiple software systems and platforms. This means they’re granted far-reaching access – to sensitive data, business-critical applications, tokens and APIs, and IT and security tools. With this access comes risk for security leaders – 92% are concerned about the use of AI agents across the workforce and their impact on security.

These agents must be governed as identities, with least-privilege access and ongoing monitoring. They can’t be thought of as invisible aspects of the application estate. Understanding how AI agents behave, and how to manage their permissions, control their behavior, and limit their data access will be a top security priority throughout 2026.

Generative AI prompts: The next frontier

Prompts are how users – both human and agentic – interact with AI systems, and they’re where natural language gets translated into model behavior. Natural language is infinite in its potential combinations and permutations, making this aspect of the attack surface open-ended and far more complex than traditional CVEs. With carefully crafted prompts, bad actors may be able to coax models into disclosing sensitive data, bypassing guardrails, or initiating undesirable actions.

Among security leaders, the biggest worries about AI usage in their environments all involve ways that systems might be manipulated to bypass traditional controls.

61% are most concerned about the exposure of sensitive data
56% are most concerned about potential data security and policy violations
51% are most concerned about the misuse or abuse of AI tools

The more employees rely on AI in their day-to-day workflows, the more critical it becomes for security teams to understand how prompt behavior determines model behavior – and where that behavior could go wrong.

What does “securing AI” mean in practice?

AI adoption opens new security risks that blur the boundaries between traditional security disciplines. A single malicious interaction with an AI model could involve identity misuse, sensitive data exposure, application logic abuse, and supply chain risk – all within a single workflow. Protecting this dynamic and rapidly evolving attack surface requires an approach that spans identity security, cloud security, application security, data security, software development security, and more.

The task for security leaders is to implement the tools, policies, and frameworks to mitigate these novel, expansive, and cross-disciplinary risks.

However, within most enterprises, AI policy creation remains in its infancy. Just 37% of security leaders report that their organization has a formal AI policy, representing a small but worrisome decrease from last year. Conversations about AI abound: in 52% of organizations, there’s discussion about an AI policy. Still, talk is cheap, and leaders will need to take action if they’re to successfully enable secure AI innovation.

To govern and protect their AI systems, organizations must take a multi-pronged approach. This requires building out policies, but it also demands that they are able to:

Monitor the prompts driving GenAI assistants and agents in real time. Organizations must be able to inspect prompts, sessions, and responses across enterprise GenAI tools, low- and high-code environments, and SaaS and SASE so that they can detect clever conversational prompt attacks and malicious chaining.
Secure all business AI agent identities. Security teams need to identify all the agents acting within their environment and supply chain, map their connections and interactions via MCP and services like Amazon S3, and audit their behavior across the cloud, SaaS environments, and on the network and endpoint devices.
Maintain centralized, comprehensive visibility. Understanding intent, assessing risks, and enforcing policies all require that security teams have a single view that spans AI interactions across the entire business.
Discover and control shadow AI. Teams need to be able to identify unsanctioned AI activities, distinguish the misuse of legitimate tools from their appropriate use, and apply policies to protect data, while guiding users towards approved solutions.

Scaling AI safely and responsibly

The approach that most cybersecurity vendors have taken – using historical patterns to predict future threats – doesn’t work well for AI systems. Because AI changes its behavior in response to the information it encounters while taking action, previous patterns don’t indicate what it will do next. Looking at past attacks can’t tell you how complex models will behave in your individual business.

Securing AI requires interpreting ambiguous interactions, uncovering subtleties that reveal intent within extended conversations, understanding how access accumulates over time, and recognizing when behavior – both human and machine – begins to drift towards areas of risk. To do this, you need to understand what “normal” looks like in each unique organization: how users, systems, applications, and AI agents behave, how they communicate, and how data flows between them.

Darktrace has spent more than a decade designing AI-powered solutions that can understand and adapt to evolving behavior in complex environments. This technology learns directly from the environment it protects, identifying malicious actions that deviate from normal operations, so that it can stop AI-related threats on the very first encounter.

As AI adoption reshapes enterprise operations, humans and machines will collaborate more and more often. This collaboration might dramatically expand the attack surface, but it also has the potential to be a force multiplier for defenders.

‍

Explore the full State of AI Cybersecurity 2026 report for deeper insights into how security leaders are responding to AI-driven risks.

Learn more about securing AI in your enterprise.

‍

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Check out this article by Darktrace: Let the Dominos Fall! SOC and IR Metrics for ROI

Let the Dominos Fall! SOC and IR Metrics for ROI

Reduction of Risk and Costs

Metrics in the Incident Response Timeline

Pre-Detection Stage

Preventions Per Intrusion Attempt

False Positive Reduction Rate

Detect and Investigate Stages

Mean Time to Detection

Mean Time to Triage

Mean Time to Understanding

Remediation Stages

Mean Time to Containment

Mean Time to Remediation / Recovery

Risk Reduction Stages

Mean Time to Advice

Mean Time to Implement

Nine Dominos

References

Phantom Footprints: Tracking GhostSocks Malware

State of AI Cybersecurity 2026: 92% of security professionals concerned about the impact of AI agents

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Blog

Network

March 26, 2026

Phantom Footprints: Tracking GhostSocks Malware

Why are attackers using residential proxies?

What is GhostSocks?

How does Ghostsocks malware work?

How does Ghostsocks malware evade detection?

Darktrace’s detection of GhostSocks Malware

Cyber AI Analyst’s investigation

Conclusion

Appendices

References

Darktrace Model Detections

MITRE ATT&CK Mapping

List of Indicators of Compromise (IoCs)

Blog

AI

March 26, 2026

State of AI Cybersecurity 2026: 92% of security professionals concerned about the impact of AI agents

AI agents introduce new risks and vulnerabilities

Generative AI prompts: The next frontier

What does “securing AI” mean in practice?

Scaling AI safely and responsibly