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March 17, 2021

AI Neutralized Hafnium-Inspired Cyber-Attacks

Learn from this real-life scenario where Darktrace detected a ProxyLogon vulnerability and took action to protect Exchange servers. Read more here.
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
Max Heinemeyer
Global Field CISO
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17
Mar 2021

On March 11 and 12, 2021, Darktrace detected multiple attempts by a broad campaign to attack vulnerable servers in customer environments. The campaign targeted Internet-facing Microsoft Exchange servers, exploiting the recently discovered ProxyLogon vulnerability (CVE-2021-26855).

While this exploit was initially attributed to a group known as Hafnium, Microsoft has announced that the vulnerability is also being rapidly weaponized by other threat actors. These new, unattributed campaigns, which have never been seen before, have been disrupted by Cyber AI in real time.

Hafnium copycats

As soon as a vulnerability is made public it is common for there to be an influx of attacks as hackers capitalize on the chaos and attempt to compromise vulnerable networks.

Patches are rapidly reverse-engineered by hackers once they have been published by the vendor, leading to mass high-impact exploits. At the same time, the offensive tooling trickles down from the first adopters, such as nation-state actors, to ransomware gangs and other opportunistic attackers. Darktrace has observed this exact phenomenon as a result of Hafnium’s attacks against vulnerable Microsoft Exchange email servers this month.

Exchange servers attacked: AI analysis

Cyber AI has observed threat actors attempting to download and install malware using ProxyLogon as the initial attack vector. For customers with Autonomous Response, the malicious payload was intercepted at this point, stopping the attack before any developments.

In other Darktrace customer environments, the Darktrace Immune System identified and alerted on every stage of the attack. Generally, the malware has been observed acting as a generic backdoor, without much follow-up activity. Various forms of command and control (C2) channels were detected, including Telegra[.]ph. In a few intrusions, the attackers installed cryptocurrency miners.

Once a foothold has been established in the digital environment, it is likely that the actors will begin a hands-on-keyboard attack, exfiltrating data, moving laterally, or deploying ransomware.

Figure 1: Timeline of a typical ProxyLogon exploit

After the ProxyLogon vulnerability was exploited, the Exchange servers reached out to the malicious domain microsoftsoftwaredownload[.]com, utilizing a PowerShell User Agent. Darktrace flagged this anomalous behavior as the particular User Agent had never been used before by the Exchange server, let alone to access a malicious domain which had never been observed in the network.

Figure 2: Darktrace revealing an anomalous PowerShell connection

The malware executable was masqueraded as a ZIP file, further trying to obfuscate the attack. Darktrace identified this highly anomalous file download and the masqueraded file.

Figure 3: Darktrace revealing key information around the anomalous file download

In some cases, Darktrace AI also observed cryptocurrency mining seconds or minutes after the initial malware download.

Figure 4: Darktrace’s Crypto Currency Mining model is breached

In terms of C2 traffic, Darktrace has observed various potential channels. Around the time of the malware download, some of the Exchange servers began to beacon out to several external destinations using unusual SSL or TLS encrypted connections.

  • Telegra[.]ph — popular messenger application
  • dev.opendrive[.]com — cloud storage service
  • od[.]lk — cloud storage service

In this case, Darktrace recognized that none of these three external domains had ever been contacted before by anybody in the organization, let alone in a beaconing fashion. The fact that these communications started around the same time as the malware downloads strongly suggests a correlation. Darktrace’s Cyber AI Analyst automatically began an investigation into the incident, stitching together these events into one coherent narrative.

Investigating with AI

Cyber AI Analyst then automatically created a summary incident report about the activity, covering the malware download as well as the various C2 channels observed.

Figure 5: Cyber AI Analyst automatically generating a high-level incident summary

Looking at an infected Exchange server ([REDACTED].local) from a birds-eye perspective shows that Darktrace created various alerts when the attack hit. Every one of the colored dots in the graph below represents a major anomaly detected by Darktrace.

Figure 6: Darktrace reveals the anomalous number of connections and subsequent model breaches

This activity was prioritized as the most urgent incident in Cyber AI Analyst among a full week’s worth of data. In this particular organization, there were only four incidents for that week in total in Cyber AI Analyst. Such precise and clear alerting allows security teams to immediately understand the top threats facing their digital environment, without being overwhelmed by unnecessary alerts and false positives.

Machine-speed response

For customers with Darktrace Antigena, Antigena autonomously acted to block all outgoing traffic to malicious external endpoints on the relevant ports. This behavior is held for several hours to interrupt the threat actor from escalating the attack, while giving security teams time to react and remediate.

Antigena responded within seconds of the attack starting, effectively containing the attack in its earliest stage – without interrupting regular business activity (emails could still be sent and received), and despite this being a zero-day campaign.

Figure 7: Darktrace Antigena autonomously responds

Catching a zero-day exploit

This is not the first time Darktrace has stopped an attack leveraging a zero-day or a freshly released n-day vulnerability. Back in March 2020, Darktrace detected APT41 exploiting the Zoho ManageEngine vulnerability, two weeks before public attribution.

It is highly likely that there will be more cyber-criminals exploiting ProxyLogon in the wake of Hafnium. And while the recent Exchange server vulnerabilities were today’s threat, next time it might be a software or hardware supply chain attack, or a different zero-day. Novel threats are emerging every week. In this climate we now find ourselves in, where ‘known unknowns’ which are difficult or impossible to pre-define are the new norm, we need to be more adaptable and proactive than ever.

As soon as an attacker begins to exhibit unusual activity, Darktrace AI will detect it, even if there is no threat intelligence associated with the attack. This is where Darktrace works best, autonomously detecting, investigating and responding to advanced and never-before-seen threats in real time.

Learn more about the Darktrace Immune System

Example Darktrace model detections:

  • Antigena / Network / Compliance / Antigena Crypto Currency Mining Block
  • Compliance / Crypto Currency Mining Activity
  • Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block
  • Anomalous Connection / Suspicious Expired SSL
  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block
  • Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Client Block
  • Device / Initial Breach Chain Compromise
  • Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block
  • Anomalous File / Masqueraded File Transfer
  • Anomalous File / EXE from Rare External Location
  • Antigena / Network / External Threat / Antigena Suspicious File Block
  • Antigena / Network / External Threat / Antigena File then New Outbound Block
  • Antigena / Network / Significant Anomaly / Antigena Controlled and Model Breach
  • Anomalous File / Internet Facing System File Download
  • Device / New PowerShell User Agent
  • Anomalous File / Multiple EXE from Rare External Locations
  • Anomalous Connection / Powershell to Rare External


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
Max Heinemeyer
Global Field CISO

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December 22, 2025

The Year Ahead: AI Cybersecurity Trends to Watch in 2026

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Introduction: 2026 cyber trends

Each year, we ask some of our experts to step back from the day-to-day pace of incidents, vulnerabilities, and headlines to reflect on the forces reshaping the threat landscape. The goal is simple:  to identify and share the trends we believe will matter most in the year ahead, based on the real-world challenges our customers are facing, the technology and issues our R&D teams are exploring, and our observations of how both attackers and defenders are adapting.  

In 2025, we saw generative AI and early agentic systems moving from limited pilots into more widespread adoption across enterprises. Generative AI tools became embedded in SaaS products and enterprise workflows we rely on every day, AI agents gained more access to data and systems, and we saw glimpses of how threat actors can manipulate commercial AI models for attacks. At the same time, expanding cloud and SaaS ecosystems and the increasing use of automation continued to stretch traditional security assumptions.

Looking ahead to 2026, we’re already seeing the security of AI models, agents, and the identities that power them becoming a key point of tension – and opportunity -- for both attackers and defenders. Long-standing challenges and risks such as identity, trust, data integrity, and human decision-making will not disappear, but AI and automation will increase the speed and scale of the cyber risk.  

Here's what a few of our experts believe are the trends that will shape this next phase of cybersecurity, and the realities organizations should prepare for.  

Agentic AI is the next big insider risk

In 2026, organizations may experience their first large-scale security incidents driven by agentic AI behaving in unintended ways—not necessarily due to malicious intent, but because of how easily agents can be influenced. AI agents are designed to be helpful, lack judgment, and operate without understanding context or consequence. This makes them highly efficient—and highly pliable. Unlike human insiders, agentic systems do not need to be socially engineered, coerced, or bribed. They only need to be prompted creatively, misinterpret legitimate prompts, or be vulnerable to indirect prompt injection. Without strong controls around access, scope, and behavior, agents may over-share data, misroute communications, or take actions that introduce real business risk. Securing AI adoption will increasingly depend on treating agents as first-class identities—monitored, constrained, and evaluated based on behavior, not intent.

-- Nicole Carignan, SVP of Security & AI Strategy

Prompt Injection moves from theory to front-page breach

We’ll see the first major story of an indirect prompt injection attack against companies adopting AI either through an accessible chatbot or an agentic system ingesting a hidden prompt. In practice, this may result in unauthorized data exposure or unintended malicious behavior by AI systems, such as over-sharing information, misrouting communications, or acting outside their intended scope. Recent attention on this risk—particularly in the context of AI-powered browsers and additional safety layers being introduced to guide agent behavior—highlights a growing industry awareness of the challenge.  

-- Collin Chapleau, Senior Director of Security & AI Strategy

Humans are even more outpaced, but not broken

When it comes to cyber, people aren’t failing; the system is moving faster than they can. Attackers exploit the gap between human judgment and machine-speed operations. The rise of deepfakes and emotion-driven scams that we’ve seen in the last few years reduce our ability to spot the familiar human cues we’ve been taught to look out for. Fraud now spans social platforms, encrypted chat, and instant payments in minutes. Expecting humans to be the last line of defense is unrealistic.

Defense must assume human fallibility and design accordingly. Automated provenance checks, cryptographic signatures, and dual-channel verification should precede human judgment. Training still matters, but it cannot close the gap alone. In the year ahead, we need to see more of a focus on partnership: systems that absorb risk so humans make decisions in context, not under pressure.

-- Margaret Cunningham, VP of Security & AI Strategy

AI removes the attacker bottleneck—smaller organizations feel the impact

One factor that is currently preventing more companies from breaches is a bottleneck on the attacker side: there’s not enough human hacker capital. The number of human hands on a keyboard is a rate-determining factor in the threat landscape. Further advancements of AI and automation will continue to open that bottleneck. We are already seeing that. The ostrich approach of hoping that one’s own company is too obscure to be noticed by attackers will no longer work as attacker capacity increases.  

-- Max Heinemeyer, Global Field CISO

SaaS platforms become the preferred supply chain target

Attackers have learned a simple lesson: compromising SaaS platforms can have big payouts. As a result, we’ll see more targeting of commercial off-the-shelf SaaS providers, which are often highly trusted and deeply integrated into business environments. Some of these attacks may involve software with unfamiliar brand names, but their downstream impact will be significant. In 2026, expect more breaches where attackers leverage valid credentials, APIs, or misconfigurations to bypass traditional defenses entirely.

-- Nathaniel Jones, VP of Security & AI Strategy

Increased commercialization of generative AI and AI assistants in cyber attacks

One trend we’re watching closely for 2026 is the commercialization of AI-assisted cybercrime. For example, cybercrime prompt playbooks sold on the dark web—essentially copy-and-paste frameworks that show attackers how to misuse or jailbreak AI models. It’s an evolution of what we saw in 2025, where AI lowered the barrier to entry. In 2026, those techniques become productized, scalable, and much easier to reuse.  

-- Toby Lewis, Global Head of Threat Analysis

Conclusion

Taken together, these trends underscore that the core challenges of cybersecurity are not changing dramatically -- identity, trust, data, and human decision-making still sit at the core of most incidents. What is changing quickly is the environment in which these challenges play out. AI and automation are accelerating everything: how quickly attackers can scale, how widely risk is distributed, and how easily unintended behavior can create real impact. And as technology like cloud services and SaaS platforms become even more deeply integrated into businesses, the potential attack surface continues to expand.  

Predictions are not guarantees. But the patterns emerging today suggest that 2026 will be a year where securing AI becomes inseparable from securing the business itself. The organizations that prepare now—by understanding how AI is used, how it behaves, and how it can be misused—will be best positioned to adopt these technologies with confidence in the year ahead.

Learn more about how to secure AI adoption in the enterprise without compromise by registering to join our live launch webinar on February 3, 2026.  

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December 22, 2025

Why Organizations are Moving to Label-free, Behavioral DLP for Outbound Email

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Why outbound email DLP needs reinventing

In 2025, the global average cost of a data breach fell slightly — but remains substantial at USD 4.44 million (IBM Cost of a Data Breach Report 2025). The headline figure hides a painful reality: many of these breaches stem not from sophisticated hacks, but from simple human error: mis-sent emails, accidental forwarding, or replying with the wrong attachment. Because outbound email is a common channel for sensitive data leaving an organization, the risk posed by everyday mistakes is enormous.

In 2025, 53% of data breaches involved customer PII, making it the most commonly compromised asset (IBM Cost of a Data Breach Report 2025). This makes “protection at the moment of send” essential. A single unintended disclosure can trigger compliance violations, regulatory scrutiny, and erosion of customer trust –consequences that are disproportionate to the marginal human errors that cause them.

Traditional DLP has long attempted to mitigate these impacts, but it relies heavily on perfect labelling and rigid pattern-matching. In reality, data loss rarely presents itself as a neat, well-structured pattern waiting to be caught – it looks like everyday communication, just slightly out of context.

How data loss actually happens

Most data loss comes from frustratingly familiar scenarios. A mistyped name in auto-complete sends sensitive data to the wrong “Alex.” A user forwards a document to a personal Gmail account “just this once.” Someone shares an attachment with a new or unknown correspondent without realizing how sensitive it is.

Traditional, content-centric DLP rarely catches these moments. Labels are missing or wrong. Regexes break the moment the data shifts formats. And static rules can’t interpret the context that actually matters – the sender-recipient relationship, the communication history, or whether this behavior is typical for the user.

It’s the everyday mistakes that hurt the most. The classic example: the Friday 5:58 p.m. mis-send, when auto-complete selects Martin, a former contractor, instead of Marta in Finance.

What traditional DLP approaches offer (and where gaps remain)

Most email DLP today follows two patterns, each useful but incomplete.

  • Policy- and label-centric DLP works when labels are correct — but content is often unlabeled or mislabeled, and maintaining classification adds friction. Gaps appear exactly where users move fastest
  • Rule and signature-based approaches catch known patterns but miss nuance: human error, new workflows, and “unknown unknowns” that don’t match a rule

The takeaway: Protection must combine content + behavior + explainability at send time, without depending on perfect labels.

Your technology primer: The three pillars that make outbound DLP effective

1) Label-free (vs. data classification)

Protects all content, not just what’s labeled. Label-free analysis removes classification overhead and closes gaps from missing or incorrect tags. By evaluating content and context at send time, it also catches misdelivery and other payload-free errors.

  • No labeling burden; no regex/rule maintenance
  • Works when tags are missing, wrong, or stale
  • Detects misdirected sends even when labels look right

2) Behavioral (vs. rules, signatures, threat intelligence)

Understands user behavior, not just static patterns. Behavioral analysis learns what’s normal for each person, surfacing human error and subtle exfiltration that rules can’t. It also incorporates account signals and inbound intel, extending across email and Teams.

  • Flags risk without predefined rules or IOCs
  • Catches misdelivery, unusual contacts, personal forwards, odd timing/volume
  • Blends identity and inbound context across channels

3) Proprietary DSLM (vs. generic LLM)

Optimized for precise, fast, explainable on-send decisions. A DSLM understands email/DLP semantics, avoids generative risks, and stays auditable and privacy-controlled, delivering intelligence reliably without slowing mail flow.

  • Low-latency, on-send enforcement
  • Non-generative for predictable, explainable outcomes
  • Governed model with strong privacy and auditability

The Darktrace approach to DLP

Darktrace / EMAIL – DLP stops misdelivery and sensitive data loss at send time using hold/notify/justify/release actions. It blends behavioral insight with content understanding across 35+ PII categories, protecting both labeled and unlabeled data. Every action is paired with clear explainability: AI narratives show exactly why an email was flagged, supporting analysts and helping end-users learn. Deployment aligns cleanly with existing SOC workflows through mail-flow connectors and optional Microsoft Purview label ingestion, without forcing duplicate policy-building.

Deployment is simple: Microsoft 365 routes outbound mail to Darktrace for real-time, inline decisions without regex or rule-heavy setup.

A buyer’s checklist for DLP solutions

When choosing your DLP solution, you want to be sure that it can deliver precise, explainable protection at the moment it matters – on send – without operational drag.  

To finish, we’ve compiled a handy list of questions you can ask before choosing an outbound DLP solution:

  • Can it operate label free when tags are missing or wrong? 
  • Does it truly learn per user behavior (no shortcuts)? 
  • Is there a domain specific model behind the content understanding (not a generic LLM)? 
  • Does it explain decisions to both analysts and end users? 
  • Will it integrate with your label program and SOC workflows rather than duplicate them? 

For a deep dive into Darktrace’s DLP solution, check out the full solution brief.

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About the author
Carlos Gray
Senior Product Marketing Manager, Email
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