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April 15, 2021

AI Neutralizes Hafnium Cyber Attack in December 2020

Protect your business from cyber attacks with AI technology. Learn how Darktrace neutralized the Hafnium attack against Exchange servers in December 2020.
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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15
Apr 2021

In early December 2020, Darktrace AI autonomously detected and investigated a sophisticated cyber-attack that targeted a customer’s Exchange server. On March 2, 2021, Microsoft disclosed an ongoing campaign by the Hafnium threat actor group leveraging Exchange server zero-days.

Based on similarities in techniques, tools and procedures (TTPs) observed, Darktrace has now assessed with high confidence that the attack in December was the work of the Hafnium group. Although it is not possible to determine whether this attack leveraged the same Exchange zero-days as reported by Microsoft, the finding suggests that Hafnium’s campaign was active several months earlier than assumed.

As a result, organizations may want to go back as far as early December 2020 to check security logs and tools for signs of initial intrusion into their Internet-facing Exchange servers.

As Darktrace does not rely on rules or signatures, it doesn’t require a constant cloud connection. Most customers therefore operate our technology themselves, and we don’t centrally monitor their detections.

At the time of detection in December, this was one of many uncategorized, sophisticated intrusions that affected only a single customer, and was not indicative of a broader campaign.

This means that while we protect our customers from individual intrusions, we are not in a position to do global campaign tracking like other companies which focus primarily on threat intelligence and threat actor tracking.

In this blog, we will analyze the attack to aid organizations in their ongoing investigations, and to raise awareness that the Hafnium campaign may have been active for longer than previously disclosed.

Overview of the Exchange attack

The intrusion was detected at an organization in the critical national infrastructure sector in South Asia. One hypothesis is that the Hafnium group was testing out and refining its TTPs, potentially including the Exchange server exploit, before running a broad-scale campaign against Western organizations in early 2021.

The threat actor used many of the same techniques that were observed in the later Hafnium attacks, including the deployment of the low-activity China Chopper web shell, quickly followed by post-exploitation activity – attempting to move laterally and spread to critical devices in the network.

The following analysis demonstrates how Darktrace’s Enterprise Immune System detected the malicious activity, how Cyber AI Analyst automatically investigated on the incident and surfaced the alert as a top priority, and how Darktrace RESPOND (formerly known as 'Antigena') would have responded autonomously to shut down the attack, had it been in active mode.

All the activity took place in early December 2020, almost three months before Microsoft released information about the Hafnium campaign.

Figure 1: Timeline of the attack from early December 2020

Initial compromise

Unfortunately, the victim organization did not keep any logs or forensic artefacts from their Exchange server in December 2020, which would have allowed Darktrace to ascertain the exploit of the zero-day. However, there is circumstantial evidence suggesting that these Exchange server vulnerabilities were abused.

Darktrace observed no signs of compromise or change in behavior from the Internet-facing Exchange server – no prior internal admin connections, no broad-scale brute-force attempts, no account takeovers, no malware copied to the server via internal channels – until all of a sudden, it began to scan the internal network.

While this is not conclusive evidence that no other avenue of initial intrusion was present, the change in behavior on an administrative level points to a complete takeover of the Exchange server, rather than the compromise of a single Outlook Web Application account.

To conduct a network scan from an Exchange server, a highly privileged, operating SYSTEM-level account is required. The patch level of the Exchange server at the time of compromise appears to have been up-to-date, at least not offering a threat actor the ability to target a known vulnerability to instantly get SYSTEM-level privileges.

For this reason, Darktrace has inferred that the Exchange server zero-days that became public in early March 2021 were possibly being used in this attack observed in early December 2020.

Internal reconnaissance

As soon as the attackers gained access via the web shell, they used the Exchange server to scan all IPs in a single subnet on ports 80, 135, 445, 8080.

This particular Exchange server had never made such a large number of new failed internal connections to that specific subnet on those key ports. As a result, Darktrace instantly alerted on the anomalous behavior, which was indicative of a network scan.

Autonomous Response

Darktrace RESPOND was in passive mode in the environment, so was not able to take action. In active mode, it would have responded by enforcing the previously learned, normal ‘pattern of life’ of the Exchange server – allowing the server to continue normal business operations (sending and receiving emails) but preventing the network scan and any subsequent activity. These actions would have been carried out via various integrations with the customer’s existing security stack, including Firewalls and Network Access Controls.

Specifically, when the network scanning started, the ‘Antigena Network Scan Block’ was triggered. This means that for several hours, Darktrace RESPOND (Antigena) would have blocked any new outgoing connections from the Exchange server to the scanned subnet on port 80, 135, 445, or 8080, preventing the infected Exchange server from conducting network scanning.

As a result, the attackers would not have been able to conclude anything from their reconnaissance — all their scanning would have returned closed ports. At this point, they would need to stop their attack or resort to other means, likely triggering further detections and further Autonomous Response.

The network scan was the first step touching the internal network. This is therefore a clear case of how Darktrace RESPOND can intercept an attack in seconds, acting at the earliest possible evidence of the intrusion.

Lateral movement

Less than an hour after the internal network scan, the compromised Exchange server was observed writing further web shells to other Exchange servers via internal SMB. Darktrace alerted on this as the initially compromised Exchange server had never accessed the other Exchange servers in this fashion over SMB, let alone writing .aspx files to Program Files remotely.

A single click allowed the security team to pivot from the alert into Darktrace’s Advanced Search, revealing further details about the written files. The full file path for the newly deployed web shells was:

Program Files\Microsoft\Exchange Server\V15\FrontEnd\HttpProxy\owa\auth\Current\themes\errorFS.aspx

The attackers thus used internal SMB to compromise further Exchange servers and deploy more web shells, rather than using the Exchange zero-day exploit again to achieve the same goal. The reason for this is clear: exploits can often be unstable, and an adversary would not want to show their hand unnecessarily if it could be avoided.

While the China Chopper web shell has been deployed with many different names in the past, the file path and file name of the actual .aspx web shell bear very close resemblance to the Hafnium campaign details published by Microsoft and others in March 2021.

As threat actors often reuse naming conventions / TTPs in coherent campaigns, it again indicates that this particular attack was in some way part of the broader campaign observed in early 2021.

Further lateral movement

Minutes later, the attacker conducted further lateral movement by making more SMB drive writes to Domain Controllers. This time the attackers did not upload web shells, but malware, in the form of executables and Windows .bat files.

Darktrace alerted the security team as it was extremely unusual for the Exchange server and its peer group to make SMB drive writes to hidden shares to a Domain Controller, particularly using executables and batch files. The activity was presented to the team in the form of a high-confidence alert such as the anonymized example below.

Figure 2: Example graphic of Darktrace detecting unusual connectivity

The batch file was called ‘a.bat’. At this point, the security team could have created a packet capture for the a.bat file in Darktrace with the click of a button, inspecting the content and details of that script at the time of the intrusion.

Darktrace also listed the credentials involved in the activity, providing context into the compromised accounts. This allows an analyst to pivot rapidly around the data and further understand the scope of the intrusion.

Bird’s-eye perspective

In addition to detecting the malicious activity outlined above, Darktrace’s Cyber AI Analyst autonomously summarized the incident and reported on it, outlining the internal reconnaissance and lateral movement activity in a single, cohesive incident.

The organization has several thousand devices covered by Darktrace’s Enterprise Immune System. Nevertheless, over the period of one week, the Hafnium intrusion was in the top five incidents highlighted in Cyber AI Analyst. Even a small or resource-stretched security team, with only a few minutes available per week to review the highest-severity incidents, could have seen and inspected this threat.

Below is a graphic showing a similar Cyber AI Analyst incident created by Darktrace.

Figure 3: A Cyber AI Analyst report showing unusual SMB activity

How to stop a zero-day

Large scale campaigns which target Internet-facing infrastructure and leverage zero-day exploits will continue to occur regularly, and such attacks will always succeed in evading signature-based detection. However, organizations are not helpless against the next high-profile zero-day or supply chain attack.

Detecting the movements of attackers inside a system and responding to contain in-progress threats is possible before IoCs have been provided. The methods of detection outlined above protected the company against this attack in December, and the same techniques will continue to protect the company against unknown threats in the future.

Learn more about how Darktrace AI has stopped Hafnium cyber-attacks and similar threat actors

Darktrace model detections:

  • Device / New or Uncommon WMI Activity
  • Executable Uploaded to DC
  • Compliance / High Priority Compliance Model Breach
  • Compliance / SMB Drive Write
  • Antigena / Network / Insider Threat / Antigena Network Scan Block
  • Device / Network Scan - Low Anomaly Score
  • Unusual Activity / Unusual Internal Connections

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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