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March 7, 2024

Defending Against the New Normal in Cybercrime: AI

This blog outlines research & data points on the evolving threat landscape, the impact of malicious AI, and why proactive cyber readiness is essential.
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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07
Mar 2024

AI in Cyber Security

Over the last 18 months, discussions about artificial intelligence (AI) – specifically generative AI – ranged from excitement and optimism about its transformative potential to fear and uncertainty about the new risks it introduces.  

New research1 commissioned by Darktrace shows that 89 percent of IT security teams polled globally believe AI-augmented cyber threats will have a significant impact on their organization within the next two years, yet 60 percent believe they are currently unprepared to defend against these attacks. Their concerns include increased volume and sophistication of malware that targets known vulnerabilities and increased exposure of sensitive or proprietary information from using generative AI tools.  

At Darktrace, we monitor trends across our global customer base to understand how the challenges facing security teams are evolving alongside industry advancements in AI. We’ve observed that AI, automation, and cybercrime-as-a-service have increased the speed, sophistication and efficacy of cyber security attacks.  

How AI Impacts Phishing Attempts

Darktrace has observed immediate impacts on phishing, which remains one of the most common forms of attack. In April 2023, Darktrace shared research that found a 135 percent increase in ‘novel social engineering attacks’ in the first two months of 2023, corresponding with the widespread adoption of ChatGPT2. These phishing attacks showed a strong linguistic deviation – semantically and syntactically – compared to other phishing emails, which suggested to us that generative AI is providing an avenue for threat actors to craft sophisticated and targeted attacks at speed and scale. A year later, we’ve seen this trend continue. Darktrace customers received approximately 2,867,000 phishing emails in December 2023 alone, a 14 percent increase on what was observed months prior in September3. Between September and December 2023, phishing attacks that used novel social engineering techniques grew by 35 percent on average across the Darktrace customer base4.  

These observations reinforce trends that others in the industry have shared. For example, Microsoft and OpenAI recently published research on tactics, techniques, and procedures (TTPs) augmented by large language models (LLMs) that they have observed nation-state threat actors using. That includes using LLMs to draft and generate social engineering attacks, inform reconnaissance, assist with vulnerability research and more.  

The Rise of Cybercrime-as-as-a-Service

The increasing cyber challenge facing defenders cannot be attributed to AI alone. The rise of cybercrime as-a-service is also changing the dynamic. Darktrace’s 2023 End of Year Threat Report found that cybercrime-as-a-service continue to dominate the threat landscape, with malware-as-a-Service (MaaS) and ransomware-as-a-Service (RaaS) tools making up most malicious tools in use by attackers. The as-a-Service ecosystem can provide attackers with everything from pre-made malware to templates for phishing emails, payment processing systems and even helplines to enable bad actors to mount attacks with limited technical knowledge.  

These trends make it clear that attackers now have a more widely accessible toolbox that reduces their barriers.

AI Enabling Accidental Insider Threats

However, the new risks facing businesses aren’t from external threat actors alone. Use of generative AI tools within the enterprise introduces a new category of accidental insider threats. Employees using generative AI tools now have easier access to more organizational data than ever before. Even the most well-intentioned employee could unintentionally leak or access restricted, sensitive data via these tools. In the second half of 2023, we observed that approximately half of Darktrace customers had employees accessing generative AI services. As this continues to increase, organizations need policies in place to guide the use cases for generative AI tools as well as strong data governance and the ability to enforce these policies to minimize risk.  

It is inevitable that AI will increase the risks and threats facing an organization, but this is not an unsolvable challenge from a defensive perspective. While advancements in generative AI may be worsening issues like novel social engineering and creating new types of accidental insider threats, AI itself offers a strong defense.  

The Shift to Proactive Cyber Readiness

According to the World Economic Forum’s Global Cybersecurity Outlook 2024, the number of organizations that “maintain minimum viable cyber resilience is down 30 percent compared to 2023”, and “while large organizations have demonstrated gains in cyber resilience, small and medium-sized companies showed significant decline.” The importance of cyber resilience cannot be understated in the face of today’s increasingly as-a-service, automated, and AI-augmented threat landscape.  

Historically, organizations wait for incidents to happen and rely on known attack data for threat detection and response, making it nearly impossible to identify never-before-seen threats. The traditional security stack has also relied heavily on point solutions focused on protecting different pieces of the digital environment, with individual tools for endpoint, email, network, on-premises data centers, SaaS applications, cloud, OT and beyond. These point solutions fail to correlate disparate incidents to form a complete picture of an orchestrated attack. Even with the addition of tools that can stitch together events from across the enterprise, they are in a reactive state that focuses heavily on threat detection and response.  

Organizations need to evolve from a reactive posture to a stance of proactive cyber readiness. To do so, they need an approach that proactively identifies internal and external vulnerabilities, identifies gaps in security policy and process before an attack occurs, breaks down silos to investigate all threats (known and unknown) during an attack, and uplifts the human analyst beyond menial tasks to incident validation and recovery after an attack.  

AI can help break down silos within the SOC and provide a more proactive approach to scale up and augment defenders. It provides richer context when it is fed information from multiple systems, data sets, and tools within the stack and can build an in-depth, real-time behavioural understanding of a business that humans alone cannot.

Lessons From AI in the SOC

At Darktrace, we’ve been applying AI to the challenge of cyber security for more than ten years, and we know that proactive cyber readiness requires the right mix of people, process, and technology.  

When the right AI is applied responsibly to the right cyber security challenge, the impact on both the human security team and the business is profound.

AI can bring machine speed and scale to some of the most time-intensive, error-prone, and psychologically draining components of cyber security, helping humans focus on the value-added work that only they can provide. Incident response and continuous monitoring are two areas where AI has already been proven to effectively augment defenders. For example, a civil engineering company used Darktrace’s AI to uplift its SOC team from the repetitive, manual tasks of analyzing and responding to email incidents. The analysts estimated they were each spending 10 hours per week on email incident analysis. With AI autonomously analyzing and responding to email incidents, the analysts could gain approximately 20 percent of their time back to focus on proactive cyber security measures

An effective human-AI partnership is key to proactive cyber readiness and can directly benefit the work-life of defenders. It can help to reduce burnout, support data-driven decision-making, and reduce the reliance on hard-to-find, specialized talent that has created a skills shortage in cyber security for many years. Most importantly, AI can free up team members to focus on more meaningful tasks, such as compliance initiatives, user education, and sophisticated threat hunting.  

Advancements in AI are happening at a rapid pace. As we’ve already observed, attackers will be watching these developments and looking for ways to use it to their advantage. Luckily, AI has already proved to be an asset for defenders, and embracing a proactive approach to cyber resilience can help organizations increase their readiness for this next phase. Prioritizing cyber security will be an enabler of innovation and progress as AI development continues.  

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Join Darktrace on 9 April for a virtual event to explore the latest innovations needed to get ahead of the rapidly evolving threat landscape. Register today to hear more about our latest innovations coming to Darktrace’s offerings.

References

[1] The survey was undertaken by AimPoint Group & Dynata on behalf Darktrace between December 2023 & January 2024. The research polled 1773 security professionals in positions across the security team from junior roles to CISOs, across 14 countries – Australia, Brazil, France, Germany, Italy, Japan, Mexico, Netherlands, Singapore, Spain, Sweden, UAE, UK, and USA.

[2] Based on the average change in email attacks between January and February 2023 detected across Darktrace/Email deployments with control of outliers.

[3] Average calculated across Darktrace customers from 31st August to 21st December.

[4] Average calculated across Darktrace customers from 31st August to 21st December. Novel social engineering attacks use linguistic techniques that are different to techniques used in the past, as measured by a combination of semantics, phrasing, text volume, punctuation, and sentence length.

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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