Why Data Classification Isn’t Enough to Prevent Data Loss
In a world of growing data volume and diversity, protecting and keeping track of your organization’s sensitive information is increasingly complex – particularly when 63% of breaches stem from malicious insiders or human error. This blog explores how security teams can achieve visibility beyond the limits of data classification, without adding to the burden of data management.
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
Carlos Gray
Senior Product Marketing Manager, Email
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15
Apr 2025
Why today’s data is fundamentally difficult to protect
Data isn’t what it used to be. It’s no longer confined to neat rows in a database, or tucked away in a secure on-prem server. Today, sensitive information moves freely between cloud platforms, SaaS applications, endpoints, and a globally distributed workforce – often in real time. The sheer volume and diversity of modern data make it inherently harder to monitor, classify, and secure. And the numbers reflect this challenge – 63% of breaches stem from malicious insiders or human error.
This complexity is compounded by an outdated reliance on manual data management. While data classification remains critical – particularly to ensure compliance with regulations like GDPR or HIPAA – the burden of managing this data often falls on overstretched security teams. Security teams are expected to identify, label, and track data across sprawling ecosystems, which can be time-consuming and error-prone. Even with automation, rigid policies that depend on pre-defined data classification miss the mark.
From a data protection perspective, if manual or basic automated classification is the sole methodology for preventing data loss, critical data will likely slip through the cracks. Security teams are left scrambling to fill the gaps, facing compliance risks and increasing operational overhead. Over time, the hidden costs of these inefficiencies pile up, draining resources and reducing the effectiveness of your entire security posture.
What traditional data classification can’t cover
Data classification plays an important role in data loss prevention, but it's only half the puzzle. It’s designed to spot known patterns and apply labels, yet the most common causes of data breaches don’t follow rules. They stem from something far harder to define: human behavior.
Data classification is blind to nuance – it can’t grasp intent, context, or the subtle red flags that often precede a breach. And no amount of labeling, policy, or training can fully account for the reality that humans make mistakes. These problems require a system that sees beyond the data itself — one that understands how it’s being used, by whom, and in what context. That’s why Darktrace leans into its core strength: detecting the subtle symptoms of data loss by interpreting human behavior, not just file labels.
Achieving autonomous data protection with behavioral AI
Its understanding of business operations allows it to detect subtle anomalies around data movement for your use cases, whether that’s a misdirected email, an insecure cloud storage link, or suspicious activity from an insider. Crucially, this detection is entirely autonomous, with no need for predefined rules or static labels.
Fig 1: Darktrace uses its contextual understanding of each user to stop all types of sensitive or misdirected data from leaving the organization
Darktrace / EMAIL’s DLP add-on continuously learns in real time, enabling:
Automatic detection: Identifies risky data behavior to catch threats that traditional approaches miss – from human error to sophisticated insider threats.
A dynamic range of actions: Darktrace always aims to avoid business disruption in its blocking actions, but this can be adjusted according to the unique risk appetite of each customer – taking the most appropriate response for that business from a whole scale of possibilities.
Enhanced context: While Darktrace doesn’t require sensitivity data labeling, it integrates with Microsoft Purview to ingest sensitivity labels and enrich its understanding of the data – for even more accurate decision-making.
Beyond preventing data loss, Darktrace uses DLP activity to enhance its contextual understanding of the user itself. In other words, outbound activity can be a useful symptom in identifying a potential account compromise, or can be used to give context to that user’s inbound activity. Because Darktrace sees the whole picture of a user across their inbound, outbound, and lateral mail, as well as messaging (and into collaboration tools with Darktrace / IDENTITY), every interaction informs its continuous learning of normal.
With Darktrace, you can achieve dynamic data loss prevention for the most challenging human-related use cases – from accidental misdirected recipients to malicious insiders – that evade detection from manual classification. So don’t stand still on data protection – make the switch to autonomous, adaptive DLP that understands your business, data, and people.
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Interested in finding out more?
Read the full solution brief to see how Darktrace's AI-driven approach to DLP stops data loss across email and Teams
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.
Journey of a Threat: How Multi-Layered AI Works in Darktrace / EMAIL
Follow a malicious email as it moves through Darktrace / EMAIL’s multi-layered AI system, from raw data to final decision. Each layer works together to detect threats, understand intent, and take autonomous action.
How email-delivered prompt injection attacks can target enterprise AI – and why it matters
Prompt injection is a newly emerging threat, with only a handful of confirmed victims so far – targeting how AI systems use data rather than exploiting traditional software vulnerabilities. As agentic AI becomes embedded across enterprise environments, attackers may attempt to manipulate these systems through hidden instructions in everyday email content.
Security After Signatures: Operating in a World of Pre‑CVE Disclosure Exploitation, Collapsed Trust Boundaries, and Autonomous Systems
Three shifts have reshaped what it means to defend an enterprise securely.
First, exploitation often begins before defenders have a Common Vulnerabilities and Exposures (CVE) identifier, a security advisory, or an entry in the Cybersecurity and Infrastructure Security Agency's (CISA) Known Exploited Vulnerabilities (KEV) catalog.
Secondly, the trust boundary has moved beyond the network edge into identities, tokens, APIs, and Software-as-a-Service (SaaS) workflows.
Third, an increasing share of business activity is executed through automation, integrations, and AI agent-like systems that can act faster than teams can verify intent.
If your security model still relies on detecting known bad artefacts, triaging isolated alerts, and waiting for confirmation before acting, you are already behind the threat.
This is not a failure of security teams; it’s a failure of the operating model to keep pace with how the environment has changed.
A SOC built around alerts and signatures assumes that malicious activity will eventually surface as an event. In real incidents, however, the decisive evidence is rarely a single event. Instead, it is a chain of individually explainable actions that only appears malicious once you connect the dots across identity, non-human identity, cloud, email, SaaS, operational technology (OT), and network telemetry.
The defenders succeeding today observe behaviors, link them into sequences, understand what those sequences mean, and contain impact before the full story unfolds. That is the operating model the current threat environment demands.
In one example, Darktrace observed a sequence of subtle but strategically significant anomalies within a customer environment that later aligned with exploitation of CVE‑2025‑0994 in Trimble Cityworks by likely Chinese-nexus threat actors. Behavioral indicators were visible at least 18 days before public disclosure, with related anomalies emerging 40 to 50 days earlier during the intrusion window.
This case illustrates a familiar pattern: clusters of weak‑signal anomalies combing to form an actionable picture of intrusion long before a CVE is published. Such activity reflects long‑horizon, option‑preserving operator models often associated with mature state‑linked activity.
Figure 1: Darktrace’s detection of malicious exploitation of CVE 2025-0994, later tied to Chinese-nexus threat actors targeting critical national infrastructure (CNI) in the US, weeks before public disclosure.
Throughout 2025 and 2026, Darktrace has continued to observe the value of anomaly-based detections across a range of incidents.
CVE
CVE Public Disclosure Date
Darktrace Detection Date
Days Between Detection of Exploitation and CVE Public Disclosure
CVE 2025 0994 (Trimble City Works)
2025-02-06
2025-01-19
18 Days
CVE 2025-24183 (Apache)
2025-03-10
2025-02-18
20 days
CVE 2025-10035 (Fortra GoAnywhere)
2025-09-18
2025-09-11
7 days
Identity is the real control plane
The second shift is that identity has replaced perimeter as the primary control plane. As Darktrace’s Annual Threat Report 2026 illustrated, identity remains the main challenge in defending against modern intrusions. A clear example is the Adversary-in-the-Middle (AiTM) case published by Darktrace in December 2025. A phishing email led to the compromise of an Office 365 account. Session hijacking bypassed multi-factor authentication (MFA), and the compromised account was used for follow-on phishing and persistence activities including the creation of malicious email rules.
Every step in that sequence mattered. A successful login alone does not prove legitimacy. An inbox rule, on its own, may not appear catastrophic. Mail activity, viewed in isolation, may seem operationally normal. But the behavioral chain tells a different story: credential theft, token abuse, persistence, and onward compromise through a trusted identity.
This is why the question is no longer “Did the user authenticate successfully”. The more important question is, “Does this identity action make sense right now, in this context, given what came before it?” The AiTM case shows how identity can be compromised. In practice, however, attacks rarely remained confined to identity alone.
In another Darktrace case, a compromised SaaS account triggered activity across the email, SaaS, and network layers, including inbox rule changes, phishing propagation, and connections to suspicious infrastructure. Viewed in isolation, none of these events were decisive. Together, however, they formed a behavioral sequence that revealed the intrusion, with the full attack story automatically correlated and surfaced to defenders by Darktrace’s Cyber AI Analyst.
Figure 2: Cyber AI Analyst correlated and appended additional events to the incident, including other users who connected to the suspicious redirect link after outbound phishing emails were sent.
AI accelerates the threat
The third shift is the one many teams still underestimate: trusted tooling, integrations, and AI agent-like systems can create actions that appear legitimate but are strategically dangerous.
The shift becomes clearer when examining how governments are now framing AI risk. In 2026, guidance published by CISA, UK’s National Cyber Security Centre (NCSC) and Five Eyes partners warned that agentic systems expand attack surfaces, accumulate privilege, and can behave in ways that are difficult to predict or explain [1]. The advice is simple: assume unexpected behavior and design controls around it.
The real risk is not AI usage. It is unknown autonomy: systems with credentials, data access, and action paths that can execute workflow steps without sufficient behavioral validation, traceability, or human oversight. Darktrace’s Model Context Protocol (MCP) risk analysis provides a useful framework for understanding this challenge. Over-privileged agents, content injection, and tool abuse become high-consequence risks when connected systems can dynamically retrieve data, execute actions, and communicate externally.
Whether security teams like it or not, AI is already in the enterprise. It will help drive innovation, but it will also be abused, whether accidentally or maliciously. In each of the cases below, AI either scaled the attacker, built the tooling, or existed within the environment as something to exploit or misuse.
1. AI as an Attack Multiplier
In one campaign targeting Mexican government entities, a single operator used commercial AI platforms to generate exploits, automate reconnaissance, and process large volumes of data, compressing work that would traditionally have required an entire team into a single workflow [2].
Attempted AI exploitation is now appearing within customer environments. In one case involving an automation technology manufacturer, a compromised LLM proxy was seemingly used as a stepping stone to access additional AI services. When that attempt failed, the attacker pivoted to cryptomining.
What is clear is that the AI layer has already become an asset worth probing, exploiting, and pivoting through. It is also clear that defenders benefit from rapidly understanding how these activities connect. In this case, Cyber AI Analyst automatically pieced together the intrusion, while Darktrace’s Managed Threat Detection service alerted to the customer, enabling the activity to be contained before it could progress further.
Figure 3: Cyber AI Analyst's investigation into a compromised LLM proxy that was abused for cryptomining activity.
AI as a trusted but dangerous actor
This does not require a cinematic vision of “rogue AI.” The Salesloft incident provides a more grounded example, where AI and automation operate with legitimate access but served malicious intent. In that case, attackers abused compromised OAuth tokens associated with the Drift AI chat agent to export significant volumes of data from Salesforce environments.
The activity resembled legitimate API usage and relied on trusted SaaS integrations rather than malware or other obvious signs of intrusion. That is precisely the challenge. Traditional security controls are good at detecting forced entry, but far less effective when a trusted application integration behaves in a way that is technically permitted yet operationally harmful.
In these scenarios, the security challenge shifts from validating access to validating behavior.
This is what that looks like in practice: AI-linked identities executing legitimate actions that require behavioral validation rather than access validation.
Figure 4: Darktrace / SECURE AI highlights anomalous activity across AI identities, surfacing critical behavior that requires validation and containment.
Early observations from Darktrace / SECURE AI deployments reinforce this reality. Across Darktrace's observed fleet, AI service connections per deployment increased 13% during the first half of 2026, reaching over 16 million connections overall. The typical organisation now interacts with seven different AI providers, evidence that AI is no longer operating at the edges of the enterprise. It is increasingly woven into day-to-day business activity.
The most common risks are not compromised models or advanced AI attacks. Instead, they stem from employees and business functions exposing sensitive information through entirely legitimate-looking interactions. Darktrace has observed repeated submission of personally identifiable information (PII), tax information, identification documents, and medical data into LLM prompts, alongside widespread use of unsanctioned (shadow) AI services and growing AI activity from mobile devices.
For defenders, the challenge is increasingly one of context: understanding when legitimate business use crosses into material risk, while preserving privacy and user trust.
Conclusion
Across all three shifts, the pattern is the same: behavior precedes understanding. Security teams are not losing because adversaries have become invisible. An increasingly outdated security model assumes that malicious activity will reveal itself cleanly and early. It no longer does.
In 2026 and beyond, defenders win by understanding behavioral sequences, continuously validating trust, and acting before certainty becomes hindsight. That is security after signatures. That is security in the AI era.
Credit to: Daniel Levy, Threat Hunting Data Scientist
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