For AppSec, security leadership, and platform teams
Clyra maps the action-control graph behind AI coding tools: workflow, credential, reachable action, target, approval rule, and evidence pack. Start with a 5-business-day assessment before a central agent registry or gateway exists.
Results in 5 business days from kickoff. Two working sessions. Local/private scan by default.
Example findings
Findings are redacted and buyer-readable. They show the path from AI-assisted work to authority, action, target, and missing evidence.
Example 01
Security can decide whether this path needs approval, credential changes, or tighter release policy.
Example 02
Platform and AppSec can see which tool paths should be registered, approved, or constrained first.
Example 03
Teams can separate real action paths from review candidates without treating every AI file as an incident.
Example 04
Engineering can keep the workflow while security focuses on the specific command, credential, and target.
What Clyra looks for
The first assessment is not a generic AI inventory. It looks for concrete software-delivery paths where AI-assisted work can reach privileged systems.
System object
Clyra connects the human owner, agent or workflow, credential, action, target, approval rule, policy decision, and evidence so security and platform teams can see where authority becomes operational.
What you get
Designed for low lift from your team: two working sessions and a local/private scan by default.
Two to three repos or workflows mapped into owner, workflow, credential, action, target, approval, and evidence paths.
AI-assisted paths that can write, execute, deploy, use credentials, or affect cloud and release workflows.
Credential and authority summary, owner/purpose gaps, and recommended allow / approve / block policy.
Buyer-readable evidence of actor, owner, credential source, approval decision, validation, outcome, and remaining gaps.
Why teams care
As AI-assisted engineering moves from suggestions into delivery, the job is to know which workflows can change real systems, what authority they use, and whether the evidence is strong enough to defend later.
Find AI-assisted workflows that create security-relevant change paths before they reach production.
Give teams AI speed without creating invisible CI/CD, credential, and release risk.
Answer a customer, auditor, or incident review with evidence, not tribal knowledge.
How it works
Clyra scans repo artifacts, CI workflows, MCP configs, agent instructions, package scripts, credential references, and PR-linked provenance when available.
It connects owner, workflow, credential, reachable action, target, approval rule, policy decision, and evidence.
Owner, purpose, approval, policy, and evidence gaps surfaced as an Agent Action BOM and evidence pack.
Field Notes
Clyra is informed by CAISI field notes on AI-assisted software delivery, agent authority, MCP/tooling, and security-control drift.
FAQ
Clyra is intentionally narrow at the start: repo artifacts, PR-linked provenance, CI/CD, tools, credentials, cloud paths, and release workflows involved in AI-assisted engineering.
Clyra helps AppSec, security engineering, and platform teams see which AI-assisted workflows can change code, run CI/CD, use credentials, or reach production paths, then shows what approval or evidence is missing.
The first assessment is designed for local or private scanning. Raw source is not retained unless explicitly agreed. The output is a redacted action-control report, not a source archive.
Yes. The early assessment is built around local/private scans so security teams can evaluate sensitive repos and delivery workflows without sending raw source to Clyra by default.
No. Secret scanning finds exposed secrets. Clyra asks a different question: which AI-assisted workflow can use a credential or token to take an action against a repo, CI/CD system, cloud path, tool, or release workflow?
The first product is visibility and assessment, not developer blocking. It helps security and platform teams decide which paths should be allowed, approved, changed, or later controlled.
Local coding assistants still change the software delivery surface when their output reaches PRs, scripts, CI jobs, MCP configs, credentials, or release paths. Clyra focuses on those delivery artifacts and action paths.
MCP is only one signal. Clyra also looks at CI workflows, package scripts, agent instructions, repo automation, credential references, GitHub Actions, cloud commands, and release-adjacent paths.
An Agent Action BOM is a buyer-readable artifact that explains which agent or workflow is acting, where it was introduced, which declared tools or systems it can reach, what credential or identity it uses, what actions are reachable, and what owner, approval, policy, or evidence gaps exist.
NHI, IAM, and PAM tools tell teams which identities and credentials exist. Runtime gateways decide whether traffic or tool calls should be allowed now. Clyra maps the upstream software delivery action-control graph: where the path came from, what authority it carries, what it can affect, and whether approval or evidence exists.
Often, yes. Clyra helps map AI-assisted delivery paths before they become normal infrastructure, so teams know what should be registered, approved, governed, or later enforced.
It is for security engineering, AppSec, platform security, DevSecOps, developer productivity, and engineering leaders at software companies already allowing AI-assisted workflows into PRs, CI/CD, tools, credentials, or cloud paths.
Design partners
If AI-assisted workflows are already close to your PRs, CI/CD, tools, credentials, or cloud paths, Clyra is looking for evaluation partners.
