01
Detect Real Issues
Continuously ingest signals from code scanners, cloud posture tools, runtime systems, deployment pipelines, and AI agent actions — filtered by context so only real risks surface.
Active Remediation · The Category
Active Remediation
for the Agentic Enterprise
Detect, diagnose, remediate, and verify risks and operational issues across code, AI agents, software delivery, cloud, Kubernetes, and production.
Protect what humans and AI agents build, change, deploy, and run.
Active Remediation Loop
Detect
Diagnose
Remediate
Verify
Applied Across the Full Lifecycle
Code
›Agents
›Delivery
›Cloud
›Runtime
What Is Active Remediation?
What Is Active Remediation?
Active Remediation is the ability to move from a finding, alert, failed deployment, policy violation, or runtime issue to a verified fix using system context, diagnostics, policy guardrails, remediation agents, and verification.
Detection finds the problem.·Active Remediation fixes it and proves it worked.
02
Diagnose Root Cause
Before any action runs, understand root cause, blast radius, ownership, dependencies, and business impact. Diagnosis turns a raw alert into an actionable, scoped remediation decision.
03
Remediate Safely
Specialized AI agents generate fixes, create pull requests, repair configurations, and execute workflows — within policy boundaries, with human approval where the risk profile demands it.
04
Verify Outcomes
Every remediation closes with validation: the issue is resolved, no new risk was introduced, application health is preserved, and audit evidence is captured automatically.
The Problem
Why Detection Alone Is Not Enough
Finding problems is only the beginning. The gap between detection and verified resolution is where risk lives.
Findings do not fix systems
A scanner report, dashboard alert, or email notification does nothing to the underlying risk. Every day between detection and fix is a day of exposure.
Tickets create delay
Jira tickets, Slack threads, and email chains add friction. By the time a developer picks up a vulnerability, the context is stale and the blast radius has grown.
AI agents can make unsafe changes
Coding assistants, deployment bots, and autonomous agents operate at machine speed — and can introduce vulnerabilities, policy violations, or unauthorized changes.
Cloud and runtime drift continuously
Infrastructure drifts from its declared state every day. Misconfigured buckets, over-privileged roles, and vulnerable workloads accumulate faster than manual review can clear them.
Teams lack code-to-cloud context
A CVE in a dependency may be unexploitable. A misconfigured S3 bucket may back a critical service. Without context, every finding looks equally urgent and equally unclear.
Fixes are rarely verified
Even when a fix is applied, no one confirms it worked. Partial patches, missed dependencies, and silent regressions are common — and unknown until the next incident.
The Process
The Active Remediation Loop
Six stages that transform raw risk signals into verified outcomes — continuously, across every domain.
Detect
Collect risks, alerts, failures, policy violations, and unsafe actions from code, cloud, pipelines, agents, and runtime.
Diagnose
Understand root cause, blast radius, ownership, dependencies, and business impact before any action is proposed.
Plan
Select the safest remediation path — accounting for policy constraints, human approval requirements, and downstream impact.
Act
Generate fixes, create PRs, update configuration, trigger workflows, or apply compensating controls within approved scope.
Verify
Confirm the issue is resolved, no new risk was introduced, and system health — security, compliance, and operations — is preserved.
Learn
Feed remediation outcomes and operational feedback into the context engine to sharpen future detection, diagnosis, and prioritisation.
Detect→
Diagnose→
Plan→
Act→
Verify→
Learn
→repeats
Full Lifecycle Coverage
Active Remediation Across the Lifecycle
From first commit to live production — remediation at every stage, not just at the perimeter.
Code & Agents
Secure human-written and AI-generated code, agent actions, pull requests, secrets, and dependencies before they reach downstream systems.
SASTSCASecretsAI Agent CodePR Governance
Build & Supply Chain
Remediate vulnerable dependencies, unsafe artifacts, build risks, and supply chain policy violations before software leaves the build system.
SBOMArtifact SigningPipeline SecuritySupply Chain
Delivery & GitOps
Prevent risky deployments, unauthorized changes, pipeline failures, and rogue agent actions from reaching production environments.
CI/CDGitOpsDeployment GatesRollback
Cloud & Kubernetes
Fix misconfigurations, drift, exposed services, risky permissions, and Kubernetes security gaps continuously across all cloud environments.
CSPMIAMK8s SecurityTerraformHelm
Runtime & Production
Remediate production risk, runtime exposure, incidents, vulnerable workloads, and operational failures without disrupting live services.
Runtime SecurityCWPPIncident ResponseRunbooks
Human-Guided or Autonomous
Human-Guided or Autonomous
Active Remediation should adapt to risk. Low-risk fixes can be automated. High-risk production changes require human approval, policy checks, and verification evidence.
The control ladder defines how much automation is appropriate for each type of change — from a simple recommendation to fully verified autonomous execution.
Who acts at each rung
Human action or approval required
AI agent executes within policy scope
RecommendAssisted
Surface the best remediation option with full context attached.
Generate FixAgentic
AI agent drafts the patch, config fix, or runbook for review.
Create PRAgentic
Automated PR opened with fix, diagnostic context, and linked finding.
Request ApprovalGoverned
High-impact changes route through policy-defined approval workflows.
Execute ActionAutonomous
Fix applied — code merge, config push, workflow trigger, or compensating control.
Verify OutcomeVerified
Multi-layer validation: security, policy, deployment, and runtime health.
Audit EvidenceRecorded
Tamper-evident record of finding, diagnosis, actions, and verification.
Platform Capabilities
Built on Context, Diagnostics,
Agents, Actions, and Verification
Five connected capabilities that power the full Active Remediation loop.
A
FoundationB
IntelligenceC
ExecutionD
ControlE
AssuranceFoundation
Context Engine
Understands software topology, delivery pipelines, cloud infrastructure, runtime state, policy rules, ownership, and business context — so every remediation decision is fully informed.
Intelligence
Pre-Remediation Diagnostics
Diagnoses root cause, blast radius, risk score, dependency impact, and safe remediation paths before any action is proposed or executed — preventing fixes that introduce new problems.
Execution
Remediation Agents
Specialized AI agents — one per domain — generate code fixes, create pull requests, repair configurations, trigger workflows, and coordinate multi-step remediation with full contextual awareness.
Control
Safe Actions
Executes approved changes across code, cloud, delivery pipelines, Kubernetes, and runtime systems — governed by policy, scoped to approved blast radius, and always within defined control limits.
Assurance
Verification
Proves the remediation worked — security posture restored, policy compliance confirmed, no regressions introduced, application health preserved, and tamper-evident audit evidence captured.
Active vs Traditional
Active Remediation vs
Traditional Remediation
The difference isn't just speed. It's context, diagnosis, and verified outcomes.
Dimension
Traditional Remediation
Active Remediation
Starting point
Alert or finding
Context-aware detection
First response
Ticket created
Root-cause diagnostics
Triage
Manual, slow, context-free
AI-powered with full context
Remediation source
Fragmented, human-driven
AI-generated, agent-executed
Approval
None or email-based
Human or policy-controlled gates
Time to fix
Days to weeks
Minutes to hours
Fix quality
Delayed, partial, unverified
Verified fix with evidence
Learning loop
None
Audit trail and learning loop
Business Outcomes
Active Remediation Outcomes
Measurable results for security, platform, and engineering teams.
MTTR
Minutes vs days
Reduce Mean Time to Remediate
Context-aware diagnosis and AI-generated fixes cut MTTR from days to minutes by eliminating manual triage and developer context-switching.
Risk
Continuous reduction
Lower Production Risk
Continuous remediation across cloud, runtime, and Kubernetes keeps production exposure low — not just measured and reported, but resolved.
Velocity
Security as enabler
Accelerate Secure Delivery
Automated remediations and pre-deployment diagnostics remove security friction from delivery pipelines — security becomes an accelerator, not a gate.
Control
Safe AI operations
Govern AI Agent Actions
Policy guardrails and human approval workflows ensure AI agents operate within defined boundaries — unlocking automation without sacrificing oversight.
Signal
Quality over volume
Reduce Alert Fatigue
Context-aware prioritisation surfaces only the findings that matter, paired with a clear remediation path — so teams act instead of triaging endlessly.
Audit
Always audit-ready
Improve Compliance Evidence
Every remediation generates a tamper-evident audit trail: finding, diagnosis, approvals, actions taken, and verification results — audit-ready by default.
Get Started
Stop Managing Findings.
Start Remediating.
Turn risks, incidents, and unsafe changes into verified remediation outcomes.
Trusted by enterprise security and platform teams
SOC 2 Type II
GDPR Compliant
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On-Prem & Cloud
SSO / SAML