01The challenge
Many risk sources. No single view. Slow fixes.
The security team had pen-test reports, dependency scans, cloud posture findings and threat intelligence, but correlating them was manual, and code fixes took days to weeks.
Fragmented risk dataFindings in different formats, aggregated by hand, so threats were spotted late.
No predictive viewAttack paths across cloud, on-premises and SaaS couldn’t be anticipated.
Slow AppSec cyclesPull-request findings and code scans took days to weeks to fix.
Developer alert fatigueDuplicate, low-priority alerts interrupted developers constantly.
02The approach
Two agents, one loop: predict the risk, then fix the code.
Using autobotAI’s workflow builder, the team built a threat-modeling agent and an AppSec remediation agent, integrated with multiple models, their development platforms and their approval channels.
Fig. 1Two multi-agent workflows
Threat-modeling agent
- Normalise & correlate
- Predict attack paths
- Prioritised risk register
- Map to assets & teams
AppSec remediation agent
- Review the pull request
- Draft the fix on a new branch
- Approve in Slack / Jira
- Learn from feedback
register
How they rolled it out
1Integrate & normaliseVA/PT, SCA, CSPM and threat intel connected without code.
2PredictThreat modeling with multiple models; prioritised risk register.
3Automate AppSecPull-request analysis, fix suggestions, orchestrated remediation.
4RefineTested in non-production; tuned on developer feedback.
03The results
Security moved left, and got faster.
73% lower MTTRFixes start at the pull request instead of weeks later.
Developers stay in flowFix suggestions arrive inside their existing workflow.
Fewer repeat vulnerabilitiesPattern detection and consistent policy stop old issues returning.
Less analyst toilCorrelation, prioritisation and first triage are automated.
Governed fixesHigh-risk fixes route through Slack and Jira approvals with audit trails.
One risk pictureSecurity, development and infrastructure share the same prioritised view.