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July 30, 2019

Digitizing the Dark: Cyber-Attacks Against Power Grids

State-sponsored cyber-attacks continue to target massive energy grids, posing a legitimate threat to modern civilization. Learn more about this threat here.
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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.
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30
Jul 2019

Among all historical discoveries, none has transformed civilization quite like electricity. From the alarm clock that wakes you up in the morning to the lights you flip off before falling asleep, the modern world has largely been made possible by electric power — a fact we tend only to reflect on with annoyance when our phones run out of battery.

However, the days of taking for granted our greatest discovery may well be nearing an end. As international conflict migrates to the digital domain, state-sponsored cyber-criminals are increasingly targeting energy grids, with the intention of causing outages that could bring victimized regions to a screeching halt. And ironically, the more advanced our illuminated world of electronics becomes, the more proficient these cyber-attacks will be at sending society back to the Dark Ages.

The light bulb goes off

On December 23, 2015, at the Prykarpattyaoblenergo power plant in Western Ukraine, a worker noticed his computer cursor quietly flitting across the screen of its own accord.

Unbeknownst to all but a select few criminals, the worker was, in fact, witnessing the dawn of a new era of cyber warfare. For the next several minutes, the cursor systematically clicked open one circuit breaker after another, leaving more than 230,000 Ukrainians without power. The worker could only watch as the cursor then logged him out of the control panel, changed his password, and shut down the backup generator at the plant itself.

As the first documented outage precipitated by a cyber-attack, the incident provoked speculation from the global intelligence community that nation-state actors had been involved, particularly given the sophisticated tactics in question. Indeed, blackouts that plunge entire cities — or even entire countries — in darkness are a devastating tactic in the geopolitical chess game. Unlike direct acts of war, online onslaughts are difficult to trace, shielding those responsible from the international backlash that accompanies military aggression. And with rival economies racing to invent the next transformative application of electricity, it stands to reason that adversaries would attempt to win that race by literally turning off the other’s lights.

Since the watershed Ukraine attack, the possibility of a similar strike has been a top-of-mind concern for governments around the globe. In March 2018, both American and European utilities were hit by a large-scale attack that could have “shut power plants off at will” if so desired, but which seemed intended instead for surveillance and intimidation purposes. While such attacks may originate in cyberspace, any escalation beyond mere warning shots would have dramatic consequences in the real world.

Smart meters, smarter criminals

Power distribution grids are sprawling, complex environments, controlled by digital systems, and composed of a vast array of substations, relays, control rooms, and smart meters. Between legacy equipment running decades-old software and new IIoT devices designed without rudimentary security controls, these bespoke networks are ripe with zero-day vulnerabilities. Moreover, because conventional cyber defenses are designed only to spot known threats facing traditional IT, they are blind to novel attacks that target such unique machines.

Among all of these machines, smart meters — which communicate electricity consumption back to the supplier — are notoriously easy to hack. And although most grids are designed to avoid this possibility, the rapid adoption of such smart meters presents a possible gateway for threat-actors seeking to access a power grid’s control system. In fact, disabling individual smart meters could be sufficient to sabotage the entire grid, even without hijacking that control system itself. Just a 1% change in electricity demand could prompt a grid to shut down in order to avoid damage, meaning that it might not take many compromised meters to reach the breaking point.

More alarming still, a large and sudden enough change in electricity demand could create a surge that inflicts serious physical damage and produces enduring blackouts. Smart energy expert Nick Hunn asserts that, in this case, “the task of repairing the grid and restoring reliable, universal supply can take years.”

Empowering the power plant

Catching suspicious activity on an energy grid requires a nuanced and evolving understanding of how the grid typically functions. Only this understanding of normalcy for each particular environment — comprised of millions of ever-changing online connections — can reveal the subtle anomalies that accompany all cyber-attacks, whether or not they’ve been seen before.

The first step is visibility: knowing what’s happening across these highly distributed networks in real time. The most effective way to do this is to monitor the network traffic generated by the control systems, as OT machines themselves rarely support security agent software. Fortunately, in most power grid architectures, these machines communicate with a central SCADA server, which can therefore provide visibility over much of the grid. However, traffic from the control system is not sufficient to see the total picture, since remote substations can be directly compromised by physical access or serve as termination points for a web of smart meters. To achieve total oversight, dedicated monitoring probes can be deployed into key remote locations.

Once you get down to this level — monitoring the bespoke and often antiquated systems inside substations — you have firmly left the world of commodity IT behind. Rather than dealing with standard Windows systems and protocols, you are now facing a jungle of custom systems and proprietary protocols, an environment that off-the-shelf security solutions are not designed to handle.

The only way to make sense of these environments is to avoid predefining what they look like, instead using artificial intelligence that self-learns to differentiate between normal and abnormal behavior for each power grid while ‘on the job’. Vendor- and protocol-agnostic, such self-learning tools are singularly capable of detecting threats against both outdated machines and new IIoT devices. And with power plants and energy grids fast becoming the next theater of cyber warfare, the switch to AI security and cybersecurity for utilities cannot come soon enough.

To learn more about how self-learning AI tools defend power grids and critical infrastructure, check out our white paper: Cyber Security for Industrial Control Systems: A New Approach.

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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.
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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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