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March 20, 2023

Multi-Factor Authentication: Not the Silver Bullet

Multi-Factor Authentication (MFA) is a widely used security measure, but it's not bulletproof. See how threat actors can exploit MFA to access your information.
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
Tegbir Singh
Cyber Analyst
Written by
Emma Foulger
Global Threat Research Operations Lead
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20
Mar 2023

Multi-Factor Authentication (MFA) is a long-established component of the identity and access management (IAM) framework that requires users to provide multiple verification factors to access Software as a Service (SaaS) and application environments, rather than simply relying on account credentials. MFA has been widely, although not universally, adopted as a security measure against common account takeover methods, such as brute-force attacks and exploiting passwords found in data leaks. Despite the adoption of MFA, account takeover methods are still prevalent across the threat landscape. However, the industry is seeing more and more examples of MFA compromise wherein threat actors exploit the security tool itself to gain account access.

Although having a security measure like MFA is a crucial first step in safeguarding a network, relying on a single method will always lead to gaps. MFA is a generic term for a broad range of products and services with varying degrees of efficacy; however, it is often used in the same way as Zero Trust, as a tick box or one size fits all solution. Knowing the gaps in security that are still present, even when utilizing effective MFA tools, is essential to mitigating the evolving threats of account compromise.

Figure 1: The standard flow process of MFA for any individual application. 

Bypassing MFA & Attack Details

Instances of threat actors’ bypassing MFA typically involve an element of social engineering, such as spamming authentication requests to the victim’s email or phone. This takes advantage of the victim’s fatigue of receiving numerous notifications, leading them to validate the request to silence the notifications. Microsoft research data published in September 2022 shows a clear trend of MFA fatigue attacks becoming increasingly popular last year [1]. Notably, the Uber hack occurred after attackers exploited this method [2]. This trend seems likely to continue as MFA progresses towards universal adoption and attackers continue to focus on social engineering as a means to bypass it. The following example details how Darktrace not only identifies and warns customers about unusual MFA activity for hijacked accounts, but also how its suite of products can take appropriate actions to prevent further compromise.

On January 5, 2023, a SaaS account belonging to a customer based in Australia was observed successfully logging in from a rare external endpoint, following two previous failed attempts. Darktrace identified that the login IP address was in the United States, which it recognized as unusual compared to the user’s expected login location, and the successful login followed multiple failed MFA authentication requests.

Figure 2: A screenshot of the SaaS console, showcasing the login activity of the SaaS user with the reason for the failed logins highlighted. 

No further suspicious activity was detected on the device, likely a tactic employed by the threat actor to remain undetected by security tools. However, a Darktrace model breach was triggered two days later following another usual login location, this time in Germany. Once again, a successful authentication request was observed, suggesting the attacker was able to consistently bypass the MFA security and access the account. 

Following this login, multiple unusual activities were observed including the access of multiple sensitive internal files and initiating updates to email folders, namely \Sent Items, \Deleted Items, and \WIISE. This type of activity is indicative of a victim’s mailbox being modified to enable attackers to send malicious spam to contacts in the organization, allowing them to escalate their privileges and move laterally throughout the network.

Figure 3: A screenshot of the SaaS console showing some of the suspicious files that were previewed by the user. 

Darktrace continued to report suspicious activity from this user with similar activity occurring again on January 8, when the user was observed logging in from another highly anomalous location and accessing similar files. The activity escalated on January 13 when, alongside an unusual login and further email updates, the user created a new email rule suspiciously named “.”.

Figure 4: A screenshot showcasing the details of the email rule that was created by the malicious actor. 

The rule appears to have targeted emails received from a specific internal user, marking them as read and moving them to a different folder; it was likely that the attacker intended to use these emails to help socially engineer third-parties and compromise the organization’s network further. Additional suspicious activity was observed from the user, including an update to an email containing a potentially sensitive attachment.

Figure 5: A screenshot showing details of the attachment observed.

Due to the combination of an unusual login and new email rule, Darktrace RESPOND/Network™ took swift autonomous action, forcing the user in question to log out and disabling the account, preventing further compromise. With the implementation of these actions the malicious actor was unable to engage in any further activity on the compromised account.

Figure 6: The above screenshot of the SaaS UI shows some of the actions initiated by Darktrace RESPOND/Network.

Conclusion

Having MFA in place is an important first step towards hardening an organization’s SaaS environment and safeguarding against less sophisticated methods of attack, however defense in depth is key to ensuring a network is truly secure. Any one security measure will always have weaknesses, and only with multiple layers of varying protection can gaps in security be effectively closed. 

Using Self-Learning AI™, Darktrace DETECT™ can quickly identify unexpected behavior on a device, even if it occurs with legitimate credentials and successfully passes MFA, to bring it to the attention of the security team. Darktrace RESPOND™ is then able to take immediate action, implementing precise actions to prevent more serious compromise. 

Pairing the two products together provides customers with an around-the-clock AI decision maker capable of detecting emerging threats, even those that would evade other traditional security measures, and interrupting attacks at machine speed with surgical precision.

Resources

[1] https://techcommunity.microsoft.com/t5/microsoft-entra-azure-ad-blog/defend-your-users-from-mfa-fatigue-attacks/ba-p/2365677 

[2] https://www.forbes.com/sites/daveywinder/2022/09/18/has-uber-been-hacked-company-investigates-cybersecurity-incident-as-law-enforcement-alerted/?sh=4d3495796056 

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
Tegbir Singh
Cyber Analyst
Written by
Emma Foulger
Global Threat Research Operations Lead

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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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