Continuously Automate Your Core Product Security Workflows

Go from manual backlog investigations and remediation to automated monitoring, prioritization, and action with Jit’s purpose-built Custom Agents.
Reachable CVEs with Easy Fixes
Before

Manually investigate every CVE to determine which ones reside in direct dependencies, which are easier to exploit and easier to patch.

After

Agent examines unstructured dependency data to identify CVEs with direct dependencies, and creates fix PRs to resolve issues.

Data sources
Codebase
SCA Scanner Findings
Package Metadata
Cloud Environment
Runtime Security Tool
Organizational Metadata

“By focusing on reachable CVEs with direct dependencies, we can make high-impact fixes with minimal effort.”

Director of AppSec at a Fintech
Application Vulnerability SLA Violations
Before

Manually updating Jira tickets, chasing owners, and reconciling SLA deadlines across multiple tools.

After

Agent automatically tracks vulnerability SLAs in Jira: real-time status, zero manual updates, full accountability.

Data sources
Issue tracking system
Security scanner findings
SLA Policies
Organizational Metadata

“Tracking vulnerability SLAs shouldn’t be a full-time job — now it’s fully automated, always accurate, and impossible to miss.”

Product Security Manager at a HealthTech company
Top Exploitable Product Security Risks
Before

Manually correlate individual scanner findings to map out exploitable attack paths created by toxic combinations.

After

Agent automatically chains together scanner findings and maps out attack paths to flag the top risks.

Data sources
Codebase
Security scanner findings
Runtime Security Tool
CI/CD Pipeline
Cloud Environment

“Instead of guessing how scanner findings combine, the Jit maps exploitable paths and prioritizes the fixes based on real exploitability.”

Staff Security Engineer at a HR Tech Company
System Weaknesses Analysis: Recurring Issues
Before

Manually digging and filtering through backlog issues to spot repeating security patterns.

After

Agent automatically identifies recurring vulnerability themes across the backlog revealing systemic weaknesses.

Data sources
Codebase
Security scanner findings
Organizational metadata
Cloud Environment
Issue tracking system

“We don’t just track vulnerabilities — we uncover the patterns that keep bringing them back.”

AppSec Leader at a Fintech
Crown Jewel Security Impact Assessment
Before

Crown jewel reviews run manually sporadically: fragmented data, outdated findings, and missed exposures.

After

Agent continuously assesses crown jewels: real-time visibility into risk, control coverage, and emerging vulnerabilities.

Data sources
Codebase
Security scanner findings
Organizational metadata
Cloud Environment
Issue tracking system

“Our most critical assets need more than periodic audits. They deserve continuous proof of security.”

DevSecOps Engineer at a Dev Tools Startup
SOC2 Report for Product Security Risks
Before

Crown jewel reviews run manually sporadically: fragmented data, outdated findings, and missed exposures.

After

Agent continuously assesses crown jewels: real-time visibility into risk, control coverage, and emerging vulnerabilities.

Data sources
GRC System
Security scanner findings
SOC 2 Requirements
Cloud Environment
Issue tracking system
DSPM
Log Data

“The SOC 2 dashboard finally connects compliance to reality — live control data, zero spreadsheets, and continuous audit readiness.”

GRC Manager at a HealthTech Startup
Security Monitoring per Development Team
Before

Crown jewel reviews run manually sporadically: fragmented data, outdated findings, and missed exposures.

After

Agent continuously assesses crown jewels: real-time visibility into risk, control coverage, and emerging vulnerabilities.

Data sources
GRC System
Security scanner findings
Organizational metadata
Cloud Environment

“Security ownership finally feels tangible. Every team can see their risk, fix it fast, and prove real progress.”

VP of Engineering at an Insurance Company
Create Your Own Custom Agent
Before

Product security teams spent hours chasing vulnerabilities in noisy backlogs: manually correlating data across scanners, tickets, and environmental components to keep up with new risks.

After

Custom agents handle the grunt work: automatically tracking, prioritizing, and acting on new vulnerabilities as fast as they appear.

Data sources
Security scanner findings
Codebase
Organizational metadata
Cloud Environment
Issue tracking system
DSPM
Log data
GRC System
Runtime security tools
Cloud configs
CMDB tools
Compliance frameworks
The internet
Package metadata
Package metadata

“We were drowning in new vulnerabilities faster than we could triage them. Now our agents do the work for us, so we can focus on real risk.”

Director of Product Security at A Gaming Company
Before

Manually investigate every CVE to determine which ones reside in direct dependencies, which are easier to exploit and easier to patch.

After

Agent examines unstructured dependency data to identify CVEs with direct dependencies, and creates fix PRs to resolve issues.

Data sources
Codebase
SCA Scanner Findings
Package Metadata
Cloud Environment
Runtime Security Tool
Organizational Metadata
Before

Manually updating Jira tickets, chasing owners, and reconciling SLA deadlines across multiple tools.

After

Agent automatically tracks vulnerability SLAs in Jira: real-time status, zero manual updates, full accountability.

Data sources
Issue tracking system
Security scanner findings
SLA Policies
Organizational Metadata
Before

Manually correlate individual scanner findings to map out exploitable attack paths created by toxic combinations.

After

Agent automatically chains together scanner findings and maps out attack paths to flag the top risks.

Data sources
Codebase
Security scanner findings
Runtime Security Tool
CI/CD Pipeline
Cloud Environment
Before

Manually digging and filtering through backlog issues to spot repeating security patterns.

After

Agent automatically identifies recurring vulnerability themes across the backlog revealing systemic weaknesses.

Data sources
Codebase
Security scanner findings
Organizational metadata
Cloud Environment
Issue tracking system
Before

Crown jewel reviews run manually sporadically: fragmented data, outdated findings, and missed exposures.

After

Agent continuously assesses crown jewels: real-time visibility into risk, control coverage, and emerging vulnerabilities.

Data sources
Codebase
Security scanner findings
Organizational metadata
Cloud Environment
Issue tracking system
Before

Crown jewel reviews run manually sporadically: fragmented data, outdated findings, and missed exposures.

After

Agent continuously assesses crown jewels: real-time visibility into risk, control coverage, and emerging vulnerabilities.

Data sources
GRC System
Security scanner findings
SOC 2 Requirements
Cloud Environment
Issue tracking system
DSPM
Log Data
Before

Crown jewel reviews run manually sporadically: fragmented data, outdated findings, and missed exposures.

After

Agent continuously assesses crown jewels: real-time visibility into risk, control coverage, and emerging vulnerabilities.

Data sources
GRC System
Security scanner findings
Organizational metadata
Cloud Environment
Before

Product security teams spent hours chasing vulnerabilities in noisy backlogs: manually correlating data across scanners, tickets, and environmental components to keep up with new risks.

After

Custom agents handle the grunt work: automatically tracking, prioritizing, and acting on new vulnerabilities as fast as they appear.

Data sources
Security scanner findings
Codebase
Organizational metadata
Cloud Environment
Issue tracking system
DSPM
Log data
GRC System
Runtime security tools
Cloud configs
CMDB tools
Compliance frameworks
The internet
Package metadata
Package metadata

“By focusing on reachable CVEs with direct dependencies, we can make high-impact fixes with minimal effort.”

Director of AppSec at a Fintech

“Tracking vulnerability SLAs shouldn’t be a full-time job — now it’s fully automated, always accurate, and impossible to miss.”

Product Security Manager at a HealthTech company

“Instead of guessing how scanner findings combine, the Jit maps exploitable paths and prioritizes the fixes based on real exploitability.”

Staff Security Engineer at a HR Tech Company

“We don’t just track vulnerabilities — we uncover the patterns that keep bringing them back.”

AppSec Leader at a Fintech

“Our most critical assets need more than periodic audits. They deserve continuous proof of security.”

DevSecOps Engineer at a Dev Tools Startup

“The SOC 2 dashboard finally connects compliance to reality — live control data, zero spreadsheets, and continuous audit readiness.”

GRC Manager at a HealthTech Startup

“Security ownership finally feels tangible. Every team can see their risk, fix it fast, and prove real progress.”

VP of Engineering at an Insurance Company

“We were drowning in new vulnerabilities faster than we could triage them. Now our agents do the work for us, so we can focus on real risk.”

Director of Product Security at A Gaming Company

How do you know Jit’s Custom AI Agents produce accurate and relevant results?

Jit’s Custom AI Agents query your Company Knowledge Graph, which is automatically generated based on the information gathered from Jit’s integrations. They only return insights grounded in this graph, ensuring findings are accurate, contextual, and aligned with your environment, policies, and priorities.

Engineering Layer

Code-to-cloud-to-runtime integrations

Security Layer

30+ security scanners integrations

Business Layer

Internal policies + compliance reqs

Company Knowledge Graph