AI-native vulnerability management
Vulnerability management that keeps evidence, owners, and fixes moving.
Boni uses AI to make vulnerability work faster to triage, easier to explain, and harder to lose. The source of truth stays evidence-led: assets, findings, owners, retests, and closure proof.
AI-assisted triage board
Evidence stays attached to every summary
Queued
New scanner records and exposure signals
Review
AI clusters plus human validation
Fixing
Owner, remediation note, retest date
Closed
Retest evidence and report update
Operating loop
The value is in the loop, not just the scan.
Collect
Bring together public exposure signals, authorized scan outputs, issue notes, asset metadata, screenshots, and remediation updates.
Normalize
Convert messy scanner output into comparable records with asset, severity, evidence, owner, status, and confidence fields.
Triage
Use AI to cluster related findings, draft summaries, suggest owners, and highlight false-positive patterns for human review.
Remediate
Move validated issues into owner queues, fix guidance, retest dates, closure notes, and leadership-ready status summaries.
AI helps teams move faster without flattening risk judgment.
Scanner outputs are noisy, developers are busy, and leadership needs clarity. Boni uses AI to compress and route the work, while retaining human review for scope, severity, validation, and disclosure.
Deterministic first
Every AI summary points back to underlying evidence: URL, asset, timestamp, scanner record, screenshot, or manual note.
Confidence-aware
Boni separates public observation, suspected issue, validated vulnerability, accepted risk, and fixed finding.
Owner-centric
The workflow is built around who has to fix the issue, when retest should happen, and what closure proof is needed.
Business-ready
Security updates can be translated into customer assurance, procurement, board, or leadership language without losing technical truth.
Repeatable
Recurring review makes the next audit cheaper because assets, patterns, false positives, and remediation decisions compound.
Boundary-safe
Active checks stay behind authorization, with explicit exclusions for destructive actions and private-data access.
Use cases
Useful wherever security findings need to become operational work.
Startup security backlog
Turn a first scanner sweep into a real risk register with owners, fix order, and customer-ready evidence.
SME exposure review
Track domains, subdomains, certificates, headers, public tooling signals, and recurring hygiene gaps.
Agency client assurance
Produce clearer scan summaries, remediation checklists, and retest notes for multiple client websites.
Product team hardening
Connect vulnerability evidence with engineering work, release timing, and customer-facing assurance needs.
Recurring review turns one audit into a compounding system.
Over time the vulnerability register learns known assets, recurring false positives, accepted risks, fix patterns, owner mappings, and evidence expectations. That makes each subsequent scan cheaper to interpret.
Evidence register
Keep raw findings and reviewed summaries together.
Remediation tracking
Track fix owner, ETA, retest, and closure state.
AI-assisted reporting
Draft summaries for engineering, leadership, and customer assurance.
FAQ
Does AI decide severity automatically?
No. AI can draft severity rationale and group evidence, but final severity should be reviewed against exploitability, impact, business context, and scope.
Can this run continuously?
Yes. The loop can run as a recurring service where public exposure checks, authorized scanner evidence, remediation updates, and retest windows are reviewed on a defined cadence.
Is this only for large enterprises?
No. The approach is designed to make vulnerability management practical for smaller teams that need discipline without heavyweight enterprise process.