Autonomy, built for real environments
Most teams don't jump straight to full automation. The safer path is earned autonomy: start with recommendations and review, then expand what the system can do as trust grows.
Autonomy is earned, not switched on
Full autopilot isn’t the right starting point. AI can move faster than your organization can trust it. Successful teams start small, validate outcomes, and expand autonomy deliberately.
A practical autonomy model for vulnerability management
This framework shows how teams can progress from manual workflows to self-healing infrastructure. Each level builds on the last.
Manual vulnerability management
Humans do everything. Vulnerability scanners report findings, but all investigation and coordination is manual.
What humans control
Security analysts review scanner outputs manually
Ownership determined by asking or checking stale CMDBs
Generic tickets created one-by-one
No systematic verification that fixes worked
AI-Assisted Investigation
The platform validates which findings are real and relevant in your environment and autonomously investigates and enriches data (e.g. asset ownership).
What AI does autonomously
Filters real vulnerabilities from false positives
Gathers asset and ownership context
Assesses exploitability
Adds business context for criticality, sensitivity, and compliance scope
What humans do
Review AI-enriched data
Prioritize and route work manually
Create and assign tickets
What’s recorded
Every inference with confidence score and sources
Believability weighting that explains how conflicts are resolved
Full agent investigation timeline
Supervised workflow automation
The platform drafts and routes tickets with full context (ownership, why it matters, steps to fix), but humans approve dispatch and actually perform the remediation steps manually.
What AI does autonomously
Bundles related vulnerabilities into actionable remediation tasks
Generates tickets with risk, business impact, and remediation steps
Routes tickets based on ownership and remediation type
Proposes SLA deadlines based on risk and policy
Recommends compensating controls when patching isn't feasible
Review and approve each ticket before it's sent
Edit ticket content, routing, or SLA
Track remediation progress
Approve exceptions and policy changes
What’s recorded
All ticket drafts with reasoning chains
Human edits and approval timestamps
Which agent generated which recommendation
Manual Remediation
The platform generates fix artifacts (e.g. PRs, IaC patches, config diffs, etc.) plus a clear explanation of impact and rollback considerations. Humans review and merge/run.
What AI does autonomously
Creates and routes tickets for pre-approved workflows
Tracks remediation progress and sends escalations for SLA breaches
Updates tickets as new data arrives
Closes tickets once fixes are verified
What humans control
Exception requests from remediation teams
Policy changes or SLA adjustments
Set confidence thresholds for automation
Perform the remediation; apply patches, update configs, deploy fixes
What’s recorded
Ticket creation with full reasoning
Escalation triggers and notifications sent
Verification checks performed and results
Autonomous Supervised Remediation
The platform can apply fixes automatically to lower environments within pre-approved guardrails and escalates exceptions or uncertainty to humans.
What AI does autonomously
Everything from Level 3, plus:
Explains impact and rollback steps
Applies fixes in dev, staging, and test environments
Verifies fixes post-deployment
What humans control
Review and merge AI-generated PRs for production
Approve deployment of fixes to production environments
Validate fixes that failed automated verification
What’s recorded
Fix generation with code diff and impact analysis
Automated deployment results in lower environments
Verification checks (passed/failed) with evidence
Self-Healing Apps and Infrastructure
Fully autonomous remediation for all environments, including production. Brings you to the full “self healing infrastructure” vision.
What AI does autonomously
Fully autonomous remediation across all environments
Applies proactive hardening based on threat intelligence
Learns from outcomes to improve future fixes
Continuous verification and automatic re-remediation if vulnerabilities reappear
What humans control
Monitor dashboards showing autonomous actions taken
Investigate anomalies flagged by AI
Adjust policies and guardrails as needed
Override any time with maintained human control
What’s recorded
Every autonomous action with complete audit trail
Verification and re-verification cycles
Policy adjustments made by system based on outcomes
Autonomy isn’t one setting
Autonomy doesn’t have to be binary across your whole environment. Teams apply different levels based on risk, environment, and impact.
Level 4-5
Low business impact, fast feedback loops valuable
Level 3-4
Moderate risk, good for piloting autonomous fixes
Level 3
Higher risk, but lower impact to customers
Level 2-3
High risk, requires approval
Level 3-4
Low risk, reversible action
Level 2-3
Well-understood, irreversible action
Level 2
High impact, requires review
Where teams get autonomy wrong
Start where you are. Scale as you trust.
Move from manual workflows to self-healing systems. One step at a time.
