Crogl, a cybersecurity startup, has been granted a new U.S. patent (US12277177B1) that could shift how organizations handle one of their biggest operational bottlenecks: the normalization of security data. The company’s technology enables its knowledge engine to analyze and correlate data from multiple systems — without forcing it into a single, standardized schema first.

For security teams already stretched thin, the ability to interrogate data as-is could mean faster investigations and less dependency on costly data engineering work.

Rethinking the Normalization Burden

Traditional security analytics depend on normalizing data from firewalls, cloud services, and endpoint logs into a unified structure before it can be analyzed. But this preprocessing step often delays insights, strips away valuable metadata, and locks organizations into specific data pipelines or vendors.

“Security operations have become a balancing act between time, talent, and tooling,” said Monzy Merza, co-founder and CEO of Crogl. “Threat actors are using AI to scale their attacks, while defenders are buried under manual work. Our goal is to augment security teams without adding overhead. This patent protects a core capability that lets customers use AI securely in their own environments — with minimal setup time.”

Crogl’s approach lets its AI-driven knowledge engine traverse heterogeneous data sources directly — from SIEMs to telemetry logs — while preserving each dataset’s native context. That means analysts can correlate events or detect anomalies across systems without waiting for normalization jobs or writing queries in multiple languages.

The Analyst’s Dilemma: Too Many Tools, Too Little Time

For practitioners on the front lines, data fragmentation has long been a daily frustration.

“As an analyst, you spend hours switching between tools just to answer basic questions,” said Filip Stojkovski, founder and lead researcher at SecOps Unpacked. “You either wait for engineers to normalize the data, which takes forever, or you learn five different query syntaxes. What we need are systems that work with the data as it exists — not as we wish it existed.”

Data normalization isn’t just tedious; it’s risky. According to Crogl, the process exposes organizations to three key challenges:

• Cost and lock-in: Consolidating data into a single store often requires expensive pipelines and can limit flexibility with vendors.

• Complexity: Analysts must remember every schema transformation and adapt to constant environmental drift.

• Loss of fidelity: Normalization can drop vendor-specific fields, making forensic work less precise.

By removing the need for normalization, Crogl’s system aims to preserve full data fidelity while speeding up analysis — something especially relevant for hybrid or multi-cloud environments, where telemetry sources are constantly evolving.

Faster Deployments, Quicker Value

Crogl says the patented capability is already integrated into its knowledge engine, allowing customers to analyze live data in place. The result, according to the company, is significantly faster onboarding. Instead of waiting days or weeks for ingestion and mapping, organizations can begin running queries and automating detections within hours.

For enterprises investing in AI-driven security, this approach could streamline adoption by letting models learn directly from unaltered operational data — without the privacy and compliance concerns that come with large-scale data movement.

Looking Ahead

The patent underscores a broader shift in cybersecurity: away from monolithic data lakes and toward systems that can understand distributed, messy, real-world data. As AI becomes a staple of both offensive and defensive cyber strategies, tools that can adapt to unstructured environments will likely play a key role in maintaining speed and visibility.

Crogl’s patent doesn’t just protect intellectual property — it signals an intent to redefine how enterprises think about data readiness in modern security operations.