Skip to content

AI in Cybersecurity

Before Google Cloud Backed gRPC for MCP, FireCompass Had Already Built It for Switchblade

Before Google Cloud Backed gRPC for MCP, FireCompass Built It for Our AI Agents

Early January, I published a deep dive into why we ripped out JSON-RPC and rewrote our Model Context Protocol (MCP) server using gRPC for our internal AI initiatives. The idea was simple: if you are building enterprise-grade agents, you cannot rely on the “guesswork” of dynamic JSON payloads. You need the strict guarantees of Protobufs.… Read More »Before Google Cloud Backed gRPC for MCP, FireCompass Built It for Our AI Agents

SEBI AI Guidelines: What 10k+ Financial Entities Must Do

SEBI AI Guidelines: What 10k+ Financial Entities Must Do

SEBI’s May 5, 2026, circular (HO/13/19/12(1)2026-ITD-1_CIMGI/10873/2026) is addressed to every category of regulated entity in the Indian securities market — exchanges, depositories, brokers, mutual funds, custodians, credit rating agencies, merchant bankers, portfolio managers, investment advisors, and more. Over 10,000 entities. The subject: AI-driven vulnerability detection tools like “Claude Mythos” and the new risk dimensions they… Read More »SEBI AI Guidelines: What 10k+ Financial Entities Must Do

Gartner Named FireCompass in the New COST Market. Here’s Why That Category Exists, and What Most Vendors Are Going to Miss

Gartner published a research note in March 2026 that quietly reshaped the offensive security market. It’s called The Future of Pen Testing Is Continuous Offensive Security Testing (Dhivya Poole, Carlos De Sola Caraballo, Mitchell Schneider, 6 March 2026, ID G00845606), and it introduces a new category: Continuous Offensive Security Testing, or COST. FireCompass was named… Read More »Gartner Named FireCompass in the New COST Market. Here’s Why That Category Exists, and What Most Vendors Are Going to Miss

Combinatorial Belief States Are the Cost of Explicit Uncertainty

Combinatorial Belief States Are the Cost of Explicit Uncertainty

Many objections to belief-state planning are framed as concerns about scalability. In practice, they are concerned about visibility. Systems that avoid explicit belief do not eliminate uncertainty; they merely conceal it. This concealment can appear efficient, but it comes at a cost that is paid later often at the point where decisions matter most. This… Read More »Combinatorial Belief States Are the Cost of Explicit Uncertainty

FireCompass IEEE Research Diagram - Belief-State Engine for Autonomous Security

(IEEE) Belief-State Engines: Solving Uncertainty in Autonomous Security Systems

FireCompass Research has published a new IEEE TecRxiv paper examining a technical weakness increasingly visible in autonomous cybersecurity systems built around large language models.  The work, Belief-State Engine: Augmenting LLMs for Principled Planning Under Partial Observability, was authored by two members of the FireCompass research team, including FireCompass co-founder and Head of Research, Arnab Chattopadhayay. … Read More »(IEEE) Belief-State Engines: Solving Uncertainty in Autonomous Security Systems

Use Cases
Technology
About
Partner
Resources

FireCompass – An EC Council Ecosystem Company.
©2025 . All Rights Reserved.