Active Remediation Platform

The Active
Remediation Platform

A context-aware AI platform that diagnoses issues, orchestrates remediation agents, executes safe actions, and verifies outcomes across the software lifecycle.

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Context-Aware Control Layer · Full Lifecycle Coverage

AI Agents
Human Approvals
Policy Gates
Code
Build
Delivery
Cloud
Runtime
Detect
Diagnose
Remediate
Verify
Platform Architecture

How the Active Remediation Platform Works

A context-aware control plane that connects actors, lifecycle stages, and intelligent engines — from raw signal to verified fix.

Remediation Labs · Active Control Plane
Actors — Who interacts with the platform
Human Operators
DevelopersSecOpsPlatform EngineersApprovers
AI Agents
Coding AgentsDeploy BotsRemediation AgentsGovernance Agents
Software Lifecycle — What the platform protects
Code
Build
Delivery
Cloud
Runtime
Active Remediation Control Plane — The platform core
Active Control Plane
01
Detect

Continuous signal ingestion across all lifecycle stages

SAST · SCA · CSPM · Runtime · Agent Actions

02
Diagnose

Root cause, blast radius, and safe remediation path

Impact Scoring · Dependency Analysis · Risk Ranking

03
Remediate

Agent-driven fix generation, PRs, and execution

Code Patches · Config Fixes · Workflow Triggers

04
Verify

Multi-layer validation before closing the finding

Security · Policy · Deployment · Runtime · Audit

Foundation Engines — Powers every remediation decision
Context Engine
Policy Engine
Workflow Orchestration
Verification Engine
Audit Trail
IntegrationsGitHub · GitLabJenkins · ArgoCDAWS · GCP · AzureKubernetesJira · ServiceNowSlack · PagerDutySnyk · Wiz · AquaTerraform · Helm
Human-initiated action
AI agent action
Verified outcome
Policy governed
Full Lifecycle Coverage

Protect What AI Builds, Changes, Deploys, and Runs

Active remediation at every stage — from the first commit to live production. Click a stage to see threats, diagnostics, remediation, and verification.

Per-stage coverage

Threats
Diagnostics
Remediation
Verification

Code & Agents

Secure what humans and AI agents write

Threats4 items
Vulnerabilities
Known CVEs in application code and inline dependencies
Exposed Secrets
API keys, tokens, and credentials committed to source
Unsafe AI-Generated Code
Security flaws introduced by coding agents and copilots
Policy Violations
Code patterns that violate security or compliance policy
Diagnostics3 items
Root Cause Analysis
Traces the vulnerability to its origin — function, import, or agent commit
Dependency Chain Audit
Maps the full transitive dependency graph to assess exposure
AI Agent Attribution
Identifies which agent action introduced the risk
Remediation3 items
Secure Code Fixes
AI-generated patches targeting the vulnerable code path
Pull Request Creation
Automated PR with fix, context, and diagnostic summary
Secret Rotation Trigger
Revokes exposed credentials and initiates rotation workflow
Verification3 items
Security Re-Scan
SAST re-runs on the patched code to confirm resolution
Policy Compliance
Validates the fix meets all security and governance policies
Audit Evidence
Tamper-evident record of finding, fix, and verification
5 Lifecycle Stages
Code to runtime coverage
AI-Powered Diagnosis
Root cause before every action
7 Specialized Agents
One agent per domain
Verified Every Time
No fix closed without validation
Platform Overview

From Findings to Verified Fixes

Five connected layers that take you from raw signal to confirmed resolution — without manual handoffs or alert fatigue.

1

Context Engine

Continuously builds a live graph of your software, delivery, cloud, and runtime topology — connecting code, teams, infrastructure, and risk signals.

2

Pre-Remediation Diagnostics

Diagnoses root cause, blast radius, policy impact, and safe fix paths before any action is taken — so remediation is precise, not reactive.

3

Remediation Agents

Specialized AI agents — one per domain — generate fixes, create PRs, repair configurations, and guide operators with full contextual awareness.

4

Actions

Executes remediation safely — recommendations, pull requests, approvals, direct changes, or automated compensating controls — governed by policy.

5

Verification

Validates every fix — confirms the issue is resolved, no regressions introduced, application health preserved, and compliance evidence captured.

Full lifecycle
Code to runtime coverage
Context-first
Every decision informed by graph data
Human-in-loop
Policy-governed approval flows
Verified always
No fix is closed until confirmed
01 — Context Engine

Context Engine

The intelligence layer behind every remediation decision.

Remediation without context is guesswork. The Context Engine continuously ingests and correlates signals from across your entire software delivery stack — building a live topology graph that maps code to owners, dependencies to deployments, and vulnerabilities to runtime impact.

Every downstream platform capability — diagnostics, agents, actions, and verification — operates on this shared context. That's what makes remediation precise, safe, and fast.

Maps code, infra, and owners into a unified graph
Updates continuously as environments change
Powers blast-radius analysis and impact scoring
Feeds policy context into every agent decision
ContextEngine
Code
Repositories
Dependencies
Build Systems
CI/CD
Artifacts
Cloud
Kubernetes
Runtime
Security Signals
Policy
Ownership
02 — Pre-Remediation Diagnostics

Diagnose Before You Remediate

Acting on a finding without understanding root cause, blast radius, and safe fix paths creates new risk. Remediation Labs diagnoses every issue in full before any action runs.

Root Cause Analysis

Foundation

Traces the origin of every finding — across code, config, dependencies, and runtime — to identify the true source, not just symptoms.

Blast Radius Analysis

Impact

Maps the full scope of impact: which services, teams, deployments, and SLAs are affected if the issue is left unresolved or fixed incorrectly.

Risk Prioritization

Prioritization

Scores every finding against business context, exploitability, runtime exposure, and ownership — so teams fix what matters first.

Dependency Impact

Dependencies

Evaluates how a proposed fix affects upstream and downstream dependencies — preventing partial fixes that introduce new instability.

Deployment Impact

Deployment

Determines whether a remediation action will affect active deployments, in-flight pipelines, or canary rollouts — before execution.

Safe Remediation Path Selection

Path

Recommends the lowest-risk remediation approach — patch, config change, compensating control, or rollback — based on full context.

Without Pre-Remediation Diagnostics

  • Fix applied without understanding blast radius
  • Dependency breaks discovered post-deploy
  • Partial patch introduces new CVE
  • Rollback required — incident extended

With Pre-Remediation Diagnostics

  • Root cause confirmed before any action
  • Safe fix path selected from full context
  • Dependency impact cleared before deployment
  • Fix verified — incident closed with evidence
03 — Remediation Agents

Specialized Remediation Agents

One agent per domain. Each trained on the context, tooling, and remediation patterns for its specific surface area. Together they cover your entire software and infrastructure lifecycle.

Code Remediation Agent

Secure what developers build and AI agents generate

  • Auto-generates dependency patches and version upgrades
  • Detects and removes exposed secrets from source code
  • Reviews and remediates AI-generated code for security flaws
  • Creates PRs with full fix context and diff explanations
SASTSCASecretsAI Code

Agent Governance Agent

Govern AI agents operating in your delivery pipeline

  • Monitors coding agents, deployment bots, and AI assistants in real time
  • Detects policy-violating actions before they merge or deploy
  • Enforces approval gates for high-risk AI-initiated changes
  • Provides audit trail for every AI agent action
AI AgentsPolicyGovernanceAudit

Code-to-Cloud Agent

Protect delivery pipelines from code to deployment

  • Detects risky pipeline changes and supply chain anomalies
  • Remediates CI/CD misconfigurations and insecure build steps
  • Fixes IaC drift between code and deployed infrastructure
  • Validates artifact integrity across the delivery chain
CI/CDIaCGitOpsSupply Chain

Cloud Remediation Agent

Fix cloud misconfigurations before they become incidents

  • Detects and remediates CSPM findings across AWS, GCP, Azure
  • Corrects over-privileged IAM roles and access policies
  • Generates Terraform/CloudFormation patches for review or auto-apply
  • Maps cloud issues back to the code or team that introduced them
CSPMIAMIaCMulti-Cloud

Kubernetes Remediation Agent

Secure and stabilize Kubernetes workloads continuously

  • Detects K8s misconfigurations, RBAC gaps, and pod security issues
  • Generates Helm/manifest patches for configuration remediation
  • Resolves workload drift and enforces security policies
  • Diagnoses cluster issues with runtime-aware context
K8s SecurityRBACHelmWorkloads

Runtime Remediation Agent

Respond to threats and anomalies in live production

  • Detects runtime anomalies, active exploits, and container drift
  • Quarantines affected workloads and applies compensating controls
  • Triggers operator runbooks with full incident context attached
  • Validates production health after every remediation action
CWPPRuntimeIncident ResponseContainers

Diagnostics Agent

Diagnose failures, incidents, and operational issues with AI

  • Diagnoses deployment failures with root cause and timeline correlation
  • Resolves Kubernetes CrashLoopBackOff, OOMKill, and scheduling issues
  • Investigates delivery problems across pipelines and environments
  • Generates runbooks and operator guidance from live context
AIOpsDeploymentIncidentsRunbooks
04 — Actions

Safe Remediation Actions

Every action in Remediation Labs is governed by context, policy, and scope. From a simple recommendation to fully autonomous execution — operators stay in control.

Human Actions
AI Agent Actions

Recommend

Assisted

Surface ranked, context-aware remediation options to the right owner with full finding context, blast radius, and fix paths attached.

Review and select the appropriate fix path
Ranks options by safety, impact, and confidence

Generate Fix

Agentic

AI agent drafts the patch, configuration change, or runbook with full context — ready for human review or direct approval.

Review generated fix before proceeding
Generates code patch, config diff, or runbook

Create Pull Request

Agentic

Automatically opens a PR with the fix applied, linked to the finding, context graph snapshot, and diagnostic summary.

Review and approve or modify the PR
Opens PR with full context, description, and links

Request Approval

Governed

Routes high-impact or policy-flagged changes through defined approval workflows before any execution occurs.

Approve, reject, or escalate the change
Routes to correct approver based on policy

Execute Change

Autonomous

Applies the fix — code merge, config push, workflow trigger, or compensating control — with full audit trail.

Manual execution or final confirmation
Autonomous execution within policy scope

Verify Outcome

Verified

Validates that the issue is resolved, no regressions were introduced, application health is preserved, and compliance evidence is captured.

Review verification report and close finding
Runs validation suite and generates evidence
05 — Verification

Verification Built In

A remediation is not complete until it is verified. Every fix goes through a multi-layer validation pass — security, policy, deployment, and runtime — before the finding is closed.

Security Validation

Re-scans the remediated component to confirm the vulnerability or misconfiguration is resolved and no new security issues were introduced.

Policy Validation

Verifies the fix complies with organizational security policies, compliance frameworks (SOC 2, PCI, CIS), and internal governance rules.

Deployment Validation

Confirms the remediation did not break deployment pipelines, in-flight rollouts, or downstream CI/CD stages — before closing the finding.

Runtime Validation

Checks live application health, workload stability, and service SLOs after the fix is applied — ensuring production integrity is preserved.

Regression Testing

Triggers targeted test suites against affected components to catch regressions introduced by the fix before they reach production.

Audit Trail

Captures a complete, tamper-evident audit record: the finding, the diagnosis, who approved, what ran, and the verification result.

Verification sequence per remediation

Fix Applied→Security Scan→Policy Check→Deploy Test→Runtime Health→Regression Suite→Audit Record→Finding Closed ✓
Why Remediation Labs

Why Remediation Labs

Six design principles that separate active remediation from everything else on the market.

Context-Aware Decisions

Every remediation is grounded in software, cloud, runtime, and business context — so actions are precise, not reactive.

Diagnostics Before Action

Root cause analysis, blast radius estimation, and safe path selection happen before any system is changed.

Agentic Remediation

Specialized AI agents generate fixes, open pull requests, repair configurations, and execute safe compensating controls.

Human-in-the-Loop Governance

Policy-defined approval gates ensure humans stay in control for high-impact changes. Autonomous only where it is safe.

Code-to-Cloud-to-Production Coverage

One platform spanning code, supply chain, delivery, cloud infrastructure, Kubernetes, and live production — no gaps.

Verification First

Every remediation must prove it worked. Security, policy, deployment, and runtime validation before a finding is closed.

Context Engine
Diagnostics
Remediation Agents
Actions
Verification
Get Started with Remediation Labs

Stop Managing Findings.
Start Remediating.

Turn alerts, risks, and incidents into verified fixes across code, cloud, and production.

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Trusted by enterprise security and platform teams

SOC 2 Type II
GDPR Compliant
Enterprise SLA
On-Prem & Cloud
SSO / SAML