Agentic security operations startup Exaforce Inc. today announced that it has raised $75 million in new funding to advance its agentic SOC Platform.

Founded in 2023, Exaforce is developing an agentic security operations center platform that combines artificial intelligence agents called “Exabots” with advanced data exploration. The goal is to give enterprises a tenfold reduction in human-led SOC work, while dramatically improving security outcomes.

The platform is designed to solve challenges in security and operations with an approach that blends large language models with semantic and behavioral models into an AI engine. The company says that unlocks accuracy, repeatability and productivity for SOCs.

The platform seeks to assist enterprises requiring an SOC solution that’s better at delivering effective and consistent responses to threats, faster at detecting and investigating issues, and cheaper to scale up defenses on demand without employing more people.

Exaforce argues that SOC analysts face a deluge of alerts, many of which are false positives. That leaves them burdened with massive datasets and manual tasks such as log stitching, user validation and ticket management, which drain resources and slow response times. Moreover, detection engineers often struggle with threat coverage for cloud environments, where native threat detection is often lacking and traditional security information and event management offers inadequate coverage.

To take on these challenges, Exaforce believes that the right AI solution for the SOC must analyze enormous volumes of logs, cloud telemetry and threat data to make rapid, high-stakes decisions. Agentic solutions that rely solely on LLMs can review only a fraction of that data at once, resulting in incomplete problem analysis and reasoning that’s unreliable and hallucination-prone.

Exaforce overcomes this technical barrier with a multi-model AI engine that is purpose-built for security and operations. The engine applies models in combination, starting with a semantic data model, along with statistical and behavioral models, to extract key insights, behaviors and relationships from raw data, then performs deeper analysis with knowledge models.

The structured use of multiple models improves the quality of SOC data, which is then fed into LLMs for end-to-end reasoning across the full scope of data. The approach avoids the blind spots of systems that use only LLMs and delivers more accurate, repeatable results.

Khosla Ventures, Mayfield and Thomvest Ventures Inc. co-led the Series A round.

“What excites us is how Exaforce is reimagining the massive opportunity of developing AI teammates to offload complex tasks that help humans increase productivity and efficacy, and they are starting with the SOC market where the problems of skills and talent are acute,” said Mayfield Managing Partner Navin Chaddha.

Image: Exaforce