TL;DR: We’re introducing the Context Intelligence Graph, a new foundational layer of our platform that evolves the Risk Intelligence Graph into an AI-native substrate.
For years, Cycode has been known for its graph foundation, the underlying fabric that connected code, pipelines, cloud assets, identities, and risks into a unified view of the Software Factory.
Today, we’re taking another defining step forward in our underlying technology that continues to see Cycode as the leader across the convergence of AST, SSCS, and ASPM.
We’re introducing the Context Intelligence Graph, a new foundational layer of our platform that evolves its existing graph foundations into an AI-native substrate. A substrate that’s purposely designed to speak to AI, provide meaning to data via decision traces to enable intelligent decision-making across the entire SDLC.
Why Convergence Unlocks Context and Context Unlocks AI in Application Security
Application security has spent the last decade chasing visibility. More dashboards. More findings. More scanners. More alerts.
But the core problem was actually fragmentation across the different disciplines.
In more modern software environments, risk flows continuously across domains. A code change triggers a pipeline. A pipeline produces an artifact. An artifact pulls in dependencies. A dependency introduces risk. That risk reaches runtime through cloud infrastructure, identity, and configuration. When risk spans systems, context cannot exist in isolation.
This is why convergence is non-negotiable, and only when application security testing, software supply chain security, and posture management converge around a shared model can a platform see the full lifecycle of risk.
Convergence is also what unlocks context.
And once context exists, application security changes fundamentally.
Understanding comes from context: where something lives, how it got there, who owns it, what it touches, whether it reaches runtime, and why it matters. Without context, visibility becomes noise. With context, signals become understanding.
The Context Intelligence Graph is what turns fragmented signals into something deeper:
a real-time, semantic understanding of your software factory, rich enough for AI to reason over, safe enough for AI to act on, and structured enough to unify the historically fragmented worlds of AST, SSCS, and ASPM into one coherent platform.
And this is what makes AI-native application security even possible.
This shift toward context-driven systems is also being recognized well beyond application security. Foundation Capital recently highlighted the importance of context graphs and decision traces as foundational building blocks for AI systems that can reason, explain decisions, and act responsibly at scale. With the Context Intelligence Graph, Cycode brings these principles into the Software Factory, where context and decision history must span across and beyond code, pipelines, cloud, and runtime.
How Cycode Models Context: The Five Context Dimensions in AppSec
Security teams already reason in context, often unconsciously.
When humans make security decisions, they don’t evaluate findings in isolation. They naturally think across a consistent set of dimensions: when something happened, what caused it, what it means, who owns it, and what happened afterward. These are the same dimensions that experienced AppSec teams use every day to determine risk.
The Context Intelligence Graph makes these implicit mental models explicit through the Five Dimension System, a structured way to model how humans already reason about security:
- Timeline – When did things change?
Understanding recency, sequence, and drift across things like code, pipelines, deployments, configuration, and vulnerabilities. - Events – What caused what?
Capturing causal chains, from pull requests to builds to deployments to incidents. - Semantics – What does this actually mean?
Normalizing meaning across scanners, tools, and human language so related risks are understood as the same underlying issue. - Attribution – Who owns or approved this?
Mapping ownership, responsibility, and authority across repositories, pipelines, services, and environments. - Outcomes – What happened after the decision?
Learning from results over time, including whether fixes reduced exposure, policies improved outcomes, or decisions led to incidents or further risk.
These five dimensions reflect how security teams already think. The Context Intelligence Graph unifies them across dimensions into a single, queryable model that AI can reason over safely and consistently.
Decision Traces: Context as Security’s Institutional Memory
Context alone is powerful. Context over time is what makes AI trustworthy.
One of the most important capabilities unlocked by a context graph is the ability to capture decision traces. Decision traces are persistent, queryable records of why security decisions were made, not just what happened.
In application security, teams make critical decisions every day:
- Why was this vulnerability prioritized
- Why was another accepted as a risk
- Why was a deployment considered safe
- Why was a policy exception approved
- Why did an incident occur
Historically, the reasoning behind these decisions is fragile. It lives in Slack threads, Jira comments, meetings, or individual memory. When teams change or time passes, that context is lost.
The Context Intelligence Graph changes this by acting as the institutional memory of application security.
Instead of storing isolated findings, the graph captures the contextual signals that explain decisions: lineage, ownership, runtime relevance, exposure, business impact, and historical outcomes. Together, these signals form decision traces that can be queried, audited, replayed, and reasoned over by both humans and AI.
What Decision Traces Look Like in Practice
Rather than capturing only point-in-time signals, decision traces preserve why a decision was made.
For example:
- Vulnerability prioritization
Traditional tools: CVE ID, severity score
With CIG: Prioritized because it was introduced in PR #443, deployed to an internet-facing revenue service, and executes at runtime.
- Deprioritizing a vulnerability
Traditional tools: Status set to accepted risk
With CIG: Deprioritized because it exists only in a non-runtime dependency and is not deployed to production.
- Safe deployment decisions
Traditional tools: Build success signal
With CIG: Deemed safe because the change impacted a single service and passed through a trusted pipeline.
- Secret rotation decisions
Traditional tools: Secret metadata
With CIG: Rotated selectively based on limited non-production usage and minimal blast radius.
- AI remediation decisions
Traditional tools: Recommendation text
With CIG: AI did not auto-remediate due to production criticality and prior remediation failures.
These decision traces transform the graph from a visibility layer into a reasoning layer.
For security teams, this means:
- Evidence paths remain explainable months later
- Audits become defensible and repeatable
- Incident reviews focus on root cause
- Knowledge survives organizational change
For AI systems, decision traces are foundational:
- AI can reference prior decisions and outcomes
- AI understands constraints and exceptions
- AI can explain why it acted or chose not to act
- AI avoids repeating past mistakes
This is how AI moves from probabilistic suggestions to accountable behavior.
Why This Makes Agentic Security Inevitable
Once application security converges around a shared context model, and once that context is preserved through decision traces, a fundamental shift occurs.
AI no longer operates blindly.
It can reason about risk with awareness of history, constraints, and consequences. It can determine not just what to do, but whether it should act at all. It can explain its decisions and learn from outcomes over time.
This is the difference between automation and autonomy. Agentic security is not a leap of faith. It is the natural result of convergence, context, and memory.
Allowing the Context Layer to Become AI-Literate
The biggest philosophical shift behind the Context Intelligence Graph is this:
It is intentionally designed to speak to AI.
This means the graph isn’t just a data store, it’s a semantic, relational model that AI agents can use to:
- sense what is happening across code, build systems, clouds, and runtime
- reason about risk with awareness of lineage, exposure, ownership, and impact
- act safely through deterministic, context-aware decisions
AI cannot do any of these things with fragmented, isolated security data.
It needs a coherent worldview, a living map. The Context Intelligence Graph is that map.
Why This Matters Now: A Market Moving Toward Convergence and an Agentic World
Analysts have been signaling a clear trend:
Security leaders want fewer tools, deeper context, and a single platform that connects the dots across the entire software lifecycle.
The once-separate categories of:
- Application Security Testing
- Software Supply Chain Security
- Application Security Posture Management
…are converging.
Risk moves across these domains continuously. Context is the connective tissue. And platforms with a unified, graph-driven understanding of risk are the ones best positioned to lead this next era.
Cycode is not catching up to this shift. We’ve been building toward it from the start.
The Context Intelligence Graph is our clearest expression of that vision in its latest evolution.
An Evolution That Unlocks What Comes Next
This evolution isn’t the end-state. It is the foundation.
The Context Intelligence Graph is the layer that future Cycode AI systems, including orchestration, autonomous decision-making, and agentic workflows, will rely on to operate safely and intelligently.
With CIG, AI will be able to:
- Understand causality, not just objects
- Infer intent, not just behavior
- Anticipate risk, not just react to it
- Take action, not just summarize
Soon, you’ll see how this foundation powers a new generation of AI capabilities across the platform.
It sets the stage for the next chapter of Cycode. And it ensures that when AI takes action, it does so with complete understanding of context, safely, intelligently, and with precision.
Welcome to the future of context-aware, AI-native application security. Welcome to Cycode’s Context Intelligence Graph.
