The False Positive Fatigue: Why Legacy DLP Fails the ROI Test 

Security teams are drowning in noise because legacy Data Loss Prevention (DLP) and Data Security Posture Management (DSPM) tools rely on error-prone pattern matching and generic classifiers that lack business context. These traditional tools were not created for the sheer amounts of data in today’s systems and therefore cannot distinguish between a legitimate business workflow and a data leak. This leads to more friction in DLP processes. 

In the Generative AI (GenAI) era, data volume is multiplying at staggering rates. At the same time, organizations are incorporating more AI tools – both company-sanctioned and unauthorized shadow AI – into their workflows. As a result, information flows involve more complex paths of humans, SaaS apps, and AI agents.

Moreover, the growing instances of data and privacy risks involving GenAI content have also markedly increased. This is resulting in an exponential number of false positive alerts in legacy DLP and DSPM tools, leading to alert fatigue and Security Operations Center (SOC) burnout, and missing or overlooking potential significant risks.   

Organizations must pivot from their existing legacy DLP tools to govern modern landscapes effectively. This means moving from basic content matching to solutions that ensure precise risk analysis that deliver human-grade accuracy. 

Issues with False Positive Overload 

A recent industry survey reveals that cybersecurity and SOC teams are being overwhelmed by the sheer number of false positive alerts that systems are creating on a day-to-day basis. According to one report, “Cybersecurity teams … are on average spending 14.1 hours per week chasing down false positive alerts due to a lack of useful visibility, tool sprawl, and outdated detection technologies…” The report noted that 73% of respondents stated that “time spent on tracking down the source of those alerts adversely impacts their ability to focus on real threats.” 

IT and SOC teams are facing constant alert fatigue. Steady false positive alerts and excessive notifications are leading to desensitization, which contributes to overwhelm, burnout, and the critical failure to notice actual incidents. This ultimately increases compliance risks as genuine security events are overlooked.

False positive alert overload and fatigue are increasing the Total Cost of Ownership (TCO).  This increased TCO stems from costly inefficiencies, such as staff being overburdened, decreased productivity, and a failure to fully capitalize on the value of deployed AI solutions.

The explosion in alerts is disrupting productivity in various points in the organization. One common example occurs when a legacy DLP tool flags a legitimate file share, causing delays and frustrations. 

These types of inefficiencies can lead to eroding trust in the organization between customers, staff, and other stakeholders over whether the organization can adequately protect data in all systems and workflows. 

The Entity-Aware Difference: Contextualizing Data Relationships 

Next-gen data security tools are evolving and moving away from basic content matching. Today's advanced solutions offer more precise risk analysis, leading to more exact results and a reduction in false positive alerts.

One critical element of advanced AI security is entity awareness, which means knowing every user, machine, app, and AI agent that is accessing sensitive resources. Through this granular context, SOC teams are able to precisely identify and recognize relationships and patterns, correlating events across disparate systems rather than reacting in isolation. 

With entity-aware tools, AI security can move beyond static detection rules, enabling AI-driven security platforms to spot nuanced behavioral anomalies and automate policy enforcement.

For example, let’s take a look at Bonfy’s core differentiator, entity-aware intelligence. Unlike tools that only scan for keywords (e.g., "confidential"), Bonfy understands the entities behind the data—the specific employees, customers, consumers, and partners involved. Bonfy uses a self-supervised Adaptive Knowledge Graph to map these relationships in real-time (e.g., knowing that User A is allowed to share data with Partner B because an NDA exists).

Further, Bonfy is able to enforce trust boundaries. Instead of merely blocking PII, it’s about ensuring Customer A’s data is never inadvertently shared with Customer B (cross-contamination) or leaked into a public model.

Case in Point: Protecting Upstream and Downstream Flows 

Here are use cases illustrating Bonfy’s precise risk analysis, in both upstream and downstream data flows:

Upstream Precision (The Prompt): A user pastes a transcript into Microsoft 365 Copilot. In this case, a legacy DLP tool detects nothing wrong. However, Bonfy detects entity-specific PII (e.g., patient data) entering a model that retains data, triggering an immediate block to prevent contamination.

Downstream Precision (The Output): An AI agent generates a contract draft. Bonfy analyzes the output not just for sensitive terms, but to ensure the recipient is authorized to view that specific client’s data, preventing accidental trust boundary violations. Unlike legacy tools, Bonfy detects when a user’s intent shifts from internal analysis to public dissemination, catching silent spills that static rules miss.

Entity Risk Management (ERM): Scoring Humans and Machines 

With AI-generated and driven content, the risk surface in organizations has significantly expanded to more than just human risk. However, advanced tools with ERM can quantify risk scores for not only employees, contractors, and third parties, but for AI agents as well. 

Because the analysis is entity-aware and precise, security teams can safely enable automated mitigation (such as redacting, blocking, or quarantining) without disrupting legitimate business. This human-grade level of accuracy dramatically reduces false positives, lowering the TCO and freeing analysts from manual triage. Consequently, organizations can deploy AI with greater confidence.

True AI governance requires more than just seeing data; it requires understanding the business logic and identities governing that data. Bonfy Adaptive Content Security™ (Bonfy ACS™) provides the entity-aware precision needed to secure data in motion, at rest, and in use by AI systems.

Request a demo to see how Bonfy’s entity-aware engine prevents data leaks with human-grade accuracy.