Remediation Labs helps enterprises automate security and operations work — and actively prevent modern attacks including agentic attacks, chained CVE attacks, and unknown attack paths — across code, cloud, delivery, and production.
As humans and AI agents make changes at machine speed, Remediation Labs detects what changed, diagnoses what it enables, and takes action before risk becomes impact.
Actors making changes
Application lifecycle
Events entering the platform
Remediation Labs · Intelligence Layer
Attackers no longer need to exploit one obvious weakness at a time. Defenders no longer need to manage a modest volume of work.
Attackers can now
Defenders face
Security teams must automate the work — and actively defend against attacks that emerge dynamically.
Mission 1
High-volume security and operations work can no longer be handled manually. Remediation Labs automates:
Automate the work humans can no longer keep up with.
Mission 2
Modern attacks are increasingly adaptive, chained, and agentic. Remediation Labs actively detects and responds to:
Identify the dangerous path before it becomes impact.
First automate the work. Then defend the system continuously.
Fragmented view
Security scanners
Vulnerabilities · Secrets · Misconfigurations
Cloud tools
Posture · Identities · Permissions
Observability
Incidents · Logs · Metrics
DevOps tools
Commits · Builds · Deployments · Config changes
Workflow tools
Tickets · Approvals · Tasks
One connected view
Preventing modern attacks requires answering
Remediation Labs connects evidence across the application lifecycle so AI can understand not just what is wrong, but what it means and what to do next.
Code & Supply Chain
Delivery & Cloud
Security & Runtime
Context Engine
Questions the Context Engine answers
Correlate findings across code, supply chain, cloud, delivery, and runtime to determine what is actually exploitable and what matters most.
Identify root cause, blast radius, ownership, application impact, and the best intervention point.
Generate corrective code, configuration, dependency, cloud, delivery, or runtime actions with policy and human approval where required.
Observe the resulting deployed state and confirm that the risk or operational problem is gone.
Automate security and operations work across the application lifecycle.
Detect the dangerous path before it becomes impact.
Detect risky, inappropriate, or malicious actions performed through AI agents across code, delivery, infrastructure, and production systems.
Identify when multiple vulnerabilities, permissions, configurations, and exposures combine into an exploitable attack path.
Surface emerging attack paths created by changes in system state — even when no single event looks critical in isolation.
Diagnose, contain, remediate, and verify when attack progression or incident signals are detected.
Take governed action to move the environment back toward a safer state.
The most dangerous attack may not be a single CVE.
It may be the chain.
An example of an attack path emerging from ordinary changes — and how Remediation Labs breaks it.
Existing CVE
Severity: Medium
AI Agent Adds Permission
Individually authorized
Deployment Changes Network Exposure
Policy-compliant in isolation
Runtime Credential Becomes Reachable
Risk: Elevated
Attack Path Becomes Active
Risk: Critical
No individual event had to look catastrophic. The danger emerged from the combination.
That is why active defense needs lifecycle context and continuous attack-path analysis.
A continuous loop across the full lifecycle.
Monitor material changes
Determine
Immediate response
Generate and coordinate
Confirm
Context-Aware Active Defense
Every remediation updates the system state. Every state change is reassessed.
Protect human-written and AI-generated code, repositories, dependencies, secrets, pull requests, and coding-agent actions.
Protect software delivery from risky human and AI-agent actions across build systems, artifacts, CI/CD, GitOps, infrastructure, and cloud changes.
Continuously understand and defend applications, containers, Kubernetes, identities, cloud environments, and evolving attack paths.
Diagnose security issues, deployment failures, Kubernetes problems, and operational incidents using cross-lifecycle context.
As AI agents gain the ability to write code, modify infrastructure, change permissions, and operate production systems, security decisions can no longer be based only on whether an action is technically permitted.
The platform must understand
Control ladder
Not every action should be autonomous.
Every autonomous action should be governed.
Every action is grounded in application, delivery, cloud, runtime, identity, policy, and business context.
Understand how vulnerabilities, permissions, identities, configurations, and changes combine into a dangerous path.
Determine root cause, blast radius, and safest intervention before changing the system.
Move beyond alerting into containment, remediation, and verified recovery.
Keep humans in control when risk, policy, or reversibility requires it.
No remediation is complete until the resulting state proves the issue or attack path is gone.
Automate security and operations work, detect chained and agentic attacks, and respond before risk becomes damage.
Or email us at info@remediationlabs.com
