The Signal-to-Noise Crisis in the SOC 

Security Operations Centers (SOCs) are not suffering from a lack of alerts; they are suffering from an abundance of them. And in the age of Generative AI (GenAI), when content volume is exploding, the "noise" has become deafening. 

Relying on outdated tools that use static analysis and pattern matching is no longer sufficient in today's AI-driven landscape. Traditional data security tools were created when humans were creating and accessing data. These tools were based on assumptions that fit human-actors’ access, such as static and well-defined files, relatively stable access patterns, and data flows that followed predictable business logic. 

The amount of false positive alerts is a common limitation of legacy security tools and is quickly becoming a pervasive issue. A recent study found that more than 70% of alerts were found to be false positives. When traditional tools flag every minor anomaly as a critical event, true threats get buried. This “boy who cried wolf" scenario leads to alert fatigue, or when SOC analysts become desensitized to the sheer number of alerts, increasing the likelihood of a major breach slipping through unnoticed. Alert fatigue is leading to widespread burnout of SOC analysts, according to recent surveys. One report notes that 71% of SOC analysts say they’re experiencing stress and burnout on the job. 

To survive the security landscape in the GenAI era, organizations must shift from volume-based detection to risk-based prioritization, filtering out the noise to focus on the signals that actually threaten the business.

The Hidden Economics of False Positives 

When it comes to evaluating the true cost of a security tool, organizations need to consider more than just the amount of the license fee. The Total Cost of Ownership (TCO) also includes the human capital required to operate it. Legacy Data Loss Prevention (DLP) and data security tools often generate extremely high false positive rates. Every false positive requires a highly skilled (and expensive) analyst to triage, investigate, and close.

This ongoing situation results in a waste of resources, and SOC departments become less efficient when they are spending considerable time chasing down false positives. Compounding the issue is an ongoing shortage of SOC analysts, with 70% of surveyed organizations stating they are understaffed according to industry reports.  

As organizations adopt a growing number of GenAI tools like Microsoft 365 Copilot, the volume of unstructured data grows exponentially. Relying on legacy tools that scale linearly with data volume will inevitably blow up the operational budget.

Blind to Business Logic: The Limits of Legacy Detection 

False positives occur because data security tools incorrectly identify or misinterpret legitimate activity, such as file sharing, as a potential threat. A key limitation of traditional data security or DLP solutions is that they rely on error-prone pattern matching (regular expressions) and generic classifiers, which contribute to the false positive problem. Solutions that rely on regular expressions (regex) are simply insufficient for today's data landscape, which is characterized by massive volumes of unstructured and semi-structured data.

Traditional tools also lack the business context to correctly distinguish legitimate behavior from malicious activity in many cases. For example, a legacy tool can recognize a credit card number and block it (a potential false positive). The tool lacks the business context to understand that the number is being processed by an authorized employee, in a secure application, and sent to a verified partner. 

Further, legacy tools were not designed to address all of the risks associated with AI-generated content, such as misinformation, intellectual property and customer data leaks, compliance violations, and other breaches. These risks are growing exponentially as volumes of AI-generated content continue to increase. Without understanding the who (identity), the where (channel), and the why (intent), legacy tools cannot distinguish between a standard business process and a data leak, thus forcing the SOC to manually validate every instance.

The Methodology Shift: AI-Driven Filtering and Risk Prioritization 

Moving to risk-based analysis is the way organizations must move forward with their data security solutions. Instead of treating all data violations equally, a modern security posture must assess risk severity. This involves scoring incidents based on the sensitivity of the data and the risk profile of the entity involved.

To lower TCO, the system must autonomously filter benign activities. This requires next-gen data security capabilities that understand business logic. Systems must employ Entity Risk Management (ERM) and quantify risk scores for employees and AI agents. For example, with risk scores, if a "high-risk" user interacts with sensitive data, it’s an alert. If a "low-risk" user performs a routine task, it’s a log. This dynamic adjustment is the key to reducing fatigue.

The Operational Payoff: From Reactive Triage to Strategic Governance 

By applying AI-driven filtering, organizations can reduce alert volume by orders of magnitude. This frees up analysts to move from reactive ticket closing to proactive threat hunting and strategic governance.

Reducing false positives doesn't just help the SOC; it removes friction for the business. Valid workflows are no longer blocked by rigid, static rules, allowing innovation (like GenAI adoption) to proceed without security becoming a bottleneck.

How Bonfy Delivers Risk-Based Precision 

Bonfy Adaptive Content Security™ (Bonfy ACS™) delivers risk-based precision analysis for enterprise organizations. Bonfy utilizes an Adaptive Knowledge Graph and entity-aware intelligence to understand business relationships, drastically reducing false positives and lowering TCO. 

With AI-driven filtering, Bonfy ACS separates noise from real threats and provides a cockpit view that surfaces only high-impact incidents for executive and analyst review.

Request a demo to see how Bonfy reduces alert noise and lowers TCO with risk-based prioritization.