Building the Autonomous Security Engineer
Our long-term vision is ambitious: build a system that can do the job of a security engineer, observe, reason, and act across your entire environment, autonomously. We're not there yet. But given the rate AI is moving, we think this is a real possibility, and we're building toward it deliberately.
Shannon is our starting point.
Security doesn't need more tools. It needs a system that thinks.
The average enterprise stitches together dozens of point solutions just to cover security. Each tool generates its own alerts, its own dashboards, its own data format. The result: an engineer who spends 80% of their time on integration plumbing and 20% on actual security work.
We want to invert that ratio. And we think the way to get there isn't another tool — it's a system that can reason about your security posture the way a great engineer would.
We're starting with application security. Shannon is our autonomous AppSec engineer: it scans your code, tests your running applications, identifies real vulnerabilities, and explains what to fix and why. Not alerts. Actual findings with context.
From there, we plan to expand into cloud security and eventually security operations, following the same principle at every layer: deep understanding of your environment, not just surface-level scanning.
Continuous telemetry from your codebase, applications, cloud infrastructure, and identity providers. Every signal, ingested in real time.
Map relationships across domains. Which service talks to which API, which dependency runs on which server, which permission grants access to what. Context, not just data.
Cross-domain intelligence that identifies risks no single tool could see. A permission change plus an unpatched dependency plus an exposed endpoint: that's a prioritized threat, not three separate alerts.
Automated remediation and continuous protection. Findings come with explanations and fixes, not just severity scores.
Shannon is live in production.
Shannon combines static analysis, dynamic testing, and LLM-powered reasoning to do work that used to require a senior security engineer and a handful of separate tools.
It's our first step, and it's already proving the thesis: a system with deep context makes better security decisions than a stack of disconnected scanners.
A roadmap built on compounding context.
The trajectory of AI suggests a system that can reason across your full security surface — code, cloud, identity, devices, operations — is increasingly within reach. Each layer we add makes every other layer smarter.
AppSec
Shannon handles application security end to end: code analysis, dynamic testing, vulnerability prioritization, and remediation guidance.
Cloud Security
Extend the same reasoning to cloud infrastructure: misconfigurations, IAM drift, network exposure.
Security Operations
Correlation and response across the full stack. The complete picture.
Each layer adds context to the others. An AppSec finding is more meaningful when you know the cloud environment it runs in. A cloud misconfiguration is more urgent when you know what application data flows through it. That's the compounding advantage of a unified system over a stack of point solutions.
Why I Built Keygraph
Before Keygraph, I led engineering for the HRIS and Payroll divisions at Lattice. We handled some of the most sensitive data a company can have: employee salaries, social security numbers, banking information. Security wasn't a side concern; it was the concern.
And yet, keeping that data secure meant managing 14 disconnected tools. My team spent more time wrangling integrations, triaging duplicate alerts, and maintaining security plumbing than we did on the actual security decisions that mattered. It was clear that the problem wasn't a lack of tools — it was the absence of a system that could think across all of them.
That experience is why Keygraph exists. We wanted to build the thing I wished I'd had at Lattice: a system that understands your environment holistically and makes real security decisions with full context. We started by building across identity, device management, and compliance. That work gave us a deep understanding of the security landscape, but we converged on AppSec because it's where the pain is sharpest and the technology is ready. But the vision has always been bigger: an autonomous security engineer that works across your entire stack.
We might not get all the way there. But the rate at which AI is advancing makes us believe it's worth building toward. And Shannon, today, is already doing work that would have saved me hundreds of hours at Lattice.
That's the bet. That's what we're building.
Varun
Backed by the Best
Leading investors and industry experts who believe in our vision.
