How We Built Cogent Security

The complexity of today’s threat landscape isn’t just about more CVEs; it’s about the lack of context to answer questions about risk, friction between stakeholders that results in delayed remediation, and the staggering amount of data that needs to be considered in order to avoid overwhelming security teams. We founded Cogent Security because we knew there had to be a better way.

Here's how we built the first truly AI-native security platform.

The Future of AI for Security

The model is the operator

“The network is the computer” – John Gage, Sun Microsystems, 1984

In 1984 John Gage famously coined this phrase predicting that with the proliferation of devices that had access to the network, which could extend computing power through computational offloading, people now had a way to avoid their programs being bottlenecked by limited computing power on their devices.

“The model is the operator” – Cogent Security, 2025

In 2025 we at Cogent believe that language models can extend human reasoning and judgment through decision offloading, and that automation and workflows no longer need to be bottlenecked by human context-gathering, analysis and decision-making. By offloading judgment, prioritization, and action orchestration to the model, workflows are no longer bottlenecked by human efforts.

AI becomes the operator: learning, reasoning, and taking action in real-time.

What Makes a Tool AI-Native?

AI-native tools aren’t just powered by or integrated with LLMs, they’re architected around them.

They’re designed to reason, not just respond.

At their core, these tools integrate decision offloading loops:

• Experts can shortcut complex tasks with intelligent guidance.

• Novices can perform like experts without needing years of experience.

• And at full maturity, the system operates fully autonomously: executing secure, informed, domain-specific decisions on its own.

Anatomy of an AI-Native Tool

Every AI-native tool at Cogent follows a structured interface:

• Models: LLMs that generate new, actionable information, powering AI-driven decisions, and domain-specific datasets and real-world enterprise security scenarios used to train and enhance our AI proprietary models.

• Knowledge: Both structured (via connectors to enterprise systems) and unstructured (policies, guidelines, docs, tribal knowledge) organized and retrieved contextually.

• Workspaces: Purpose-built user interfaces like editors, side panels, chat assistants, where reasoning and collaboration happen.

• Reasoning: A persistent layer that tracks states, understands user intent, and drives decisions through contextual inference.

• Enterprise Readiness: Seamless integration with security, compliance, and operations: from SSO to audit logs, to maximize support for enterprise environments.

• Secure by Design: Fully isolated multi-tenant architecture from the start, built-in rigorous access controls, robust input/output validation, and comprehensive data sanitization practices.

To unleash the defensive potential of fully autonomous AI systems, these components must be first-class citizens, designed from the ground up, and not bolted on. That’s how we build not just smarter tools, but trusted AI collaborators that think, act, and secure.

Data, AI, Agents: Ingredients for the Modern Security Platform

Your AI is only as good as the data you feed into it, and most enterprises do not have clean or up-to-date data, much less an easy way to understand if the data is good or bad. To deliver reliable, autonomous AI systems, we need to work backwards from the data, to the AI, to the Agents, and build a platform that combines all three of them.

Data: The AI Fabric for Messy Enterprise Data

Our data layer acts like a self-learning neural network—ingesting, normalizing, and connecting every signal as it happens. Our real-time lakehouse architecture, which we battle-tested at Abnormal AI by ingesting trillions of emails, lets us easily handle the live ingestion of telemetry, logs, asset changes, vulnerability feeds, and other context sources. We can immediately normalize and index every incoming event with high efficiency.

AI: Reasoning That Drives Action

Our AI layer is built around our state-of-the-art reasoning engine which drives decision offloading and is seamlessly integrated into every product and surface that we build.

Most current AI tools follow a "single shot" approach: despite advanced context handling, they rely on a single large language model (LLM) inference at their core, limiting their ability to handle tasks requiring complex reasoning or coordinated edits across multiple business stakeholders.

Cogent instead leverages proprietary small-language models fine-tuned and trained on both security and organization context to handle complex reasoning and multi-source contextualization.

Agents: Autonomous, Contextualized, Like Humans on Your Team

Our AI agents behave like expert teammates who take on security tasks from start to finish with minimal human effort. Each agent, whether it’s focused on Triage, Risk Assessment, Remediation, or another role, is trained with the right knowledge so it understands exactly what to do. When one agent finishes its part, it hands tasks off to the next specialist agent, just as colleagues would pass work along.

What makes these agents truly autonomous is that learning is built into how they operate. They continuously adapt based on outcomes, feedback, and changes in your environment. This embedded learning loop allows them to improve over time without manual retraining, making decisions that grow more accurate and context-aware with every interaction.

We designed our agents using lessons learned building large-scale AI systems at Abnormal AI and Google Research. The result is autonomous, contextualized AI Agents that operate with deep understanding of your environment and intent, working alongside security teams to drive MTTR reduction and better risk prioritization.

Security and Privacy by Design

At Cogent, security isn’t an afterthought, it’s engineered into every layer of our platform. Our team has defended some of the world’s most critical infrastructures, from safeguarding trillions in financial assets at Coinbase and Blackstone, to securing mission-critical fleets at Tesla, to exposing nation-state attackers at Abnormal AI. We bring that same uncompromising adversarial mindset here.

Closing Thoughts

Vulnerability management has been stuck in the past: reactive, repetitive, and painfully manual. Cogent Security changes that.

We’ve assembled a world-class team of cybersecurity and AI builders. These are the people who have secured global financial systems, exposed advanced nation-state threat actors, and shipped production-grade AI infrastructure at scale. That same depth of experience is now focused on one mission: to build autonomous systems that make vulnerability management intelligent, efficient, and continuous.

With Cogent, your team doesn’t just react to risk. It gets ahead of it. Our AI agents reason through context, prioritize what matters, and drive remediation across every asset and every vulnerability, all in real time.

This is the future of cybersecurity. Autonomous and hands-free, allowing humans to focus on the most strategic work.