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DEVSECOPS
A smarter way to build, secure, and deliver software
Legacy complexity, late-stage security, disconnected AI tools — these aren't new problems, but they're getting more expensive to ignore. ByteSource integrates AI, security, and continuous delivery into one engineering pipeline, built around the tools your organisation already relies on.
AI integrated across every stage
From analysing your existing codebase to generating production-ready code, AI tools like Claude Code, Rovo Dev, and Kiro are embedded into planning, development, and testing — not bolted on as a side tool.
Security that runs with the pipeline
Dependency scanning, SAST, DAST, and secret detection run automatically on every commit. Vulnerabilities are blocked before merge, not discovered in production.
Living documentation, automatically
Architecture, features, and APIs are documented by AI based on your actual code and company guidelines — stored in Confluence, Git wikis, or your knowledge system of choice.
Full traceability, requirement to deploy
Every code change traces back to a Jira story. Every security finding routes back to a tracked ticket. Nothing happens outside the system — and nothing gets lost.
Open - no vendor lock-in
GitLab, GitHub, or Bitbucket. Jira or Azure DevOps. Snyk or SonarQube. The pipeline adapts to your existing toolchain and extends it — it doesn't replace it.
Built for modernisation, not just greenfield
ByteSource's approach is specifically designed for teams inheriting complex legacy systems — using AI to understand, document, and incrementally modernise existing applications without a full rewrite.
OPEN DEVSECOPS
Your tools. One integrated engineering workflow.
ByteSource's Open DevSecOps integration connects the best tools in the industry into one seamless, AI-powered engineering pipeline — built for teams who can't afford to slow down.
Plan
Know your code before you touch it
Most teams are flying blind through legacy systems – we change that.
⚠ Legacy System blind spots
→ Full codebase visibility
⚠ Documentation debt
→ Living documentation
⚠ Unclear technical priorities
→ Actionable modernisation plan
AI tools scan the existing codebase to map architecture, surface technical debt, identify dead code, and uncover hidden dependencies – giving teams complete visibility into complex systems before modernization begins.
Claude Code RovoDev Kiro
Features, architecture, and APIs are documented automatically from the actual codebase and stored directly in Confluence, GitHub Wiki, or your preferred knowledge platform. Documentation stays current because it’s generated continuously, not maintained manually.
Claude Code RovoDev Kiro Confluence GitHub Wiki
→ Modernisation roadmap
Analysis findings are translated into a structured delivery plan with epics, stories, and refactoring tasks created directly in Jira — prioritised by business value and technical risk, ready for sprint planning.
Claude Code Jira Confluence
→ AI skills & guidelines setup
Before AI generates a single line of code, your team defines the rules it follows. Coding standards, architecture patterns, naming conventions, and security requirements are embedded into reusable AI skills that govern every downstream stage consistently.
Claude Code RovoDev Kiro Confluence
→ Requirements & user story definition
Business requirements are transformed into structured Jira stories and acceptance criteria, creating the specification layer AI coding tools use to generate accurate and traceable implementation output.
RovoDev Jira Confluence GitHub Wiki
Develop
From specification to working code - in a fraction of the time
AI doesn’t replace developers. It removes friction so they can focus on building.
⚠ Specification-to-code handoff gaps
→ Faster implementation
⚠ Inconsistent coding practices
→ Standardised delivery
⚠ Manual Git workflows
→ End-to-end traceability
→ AI-assisted development
Production-ready code is generated from Jira stories and technical specifications using tools like Claude Code, Rovo Dev, or Kiro — aligned with your architecture patterns, coding conventions, and internal AI guidelines.
Claude Code RovoDev Kiro Cusrsor GitHub Copilot VS Code Jira Confluence
→ Branching & version control
AI-assisted branching, commit generation, and pull request creation keep repositories structured and every change traceable back to the originating requirement.
VS Code Bitbucket Jira GitLab GitHub
-> Secret & configuration management
API keys, credentials, and environment configurations are managed centrally and protected from accidental exposure throughout the development lifecycle.
HashiCorp Vault AWS Secrets Manager GitLab GitHub
→ Collaboration & communication
Development activity stays connected to discussions, documentation, and delivery decisions — reducing context switching and keeping teams aligned across the workflow.
Microsoft Teams Slack Confluence
Build
Security isn't a phase. It's the pipeline.
Vulnerabilities caught during development cost a fraction of what they cost in production.
⚠ Vulnerable dependencies enter production
→ Automated CI/CD enforcement
⚠ Security checks happen too late
→ Continuous code & dependency scanning
⚠ AI-generated code outpaces validation
→ Policy-driven vulnerability blocking
→ Continuous integration
Every code change triggers automated build and test pipelines — including unit, integration, and end-to-end testing — with results fed directly back into the merge workflow.
GitLab CI GitHub Actions Bitbucket Pipelines JFrog Artifactory
→ Dependency security scanning (SCA)
Open-source dependencies are continuously scanned for known vulnerabilities before entering the artifact registry, preventing risky packages from progressing through the pipeline.
Snyk JFrog Xray SonarQube GitLab SAST
→ Code security scanning (SAST/DAST)
Application code and deployed environments are continuously analysed for vulnerabilities, exposed secrets, and insecure patterns using static and dynamic security testing. Findings are tracked directly in Jira for remediation and governance.
Snyk SonarQube GitLab SAST Agentic QA
Test
Test at the speed of AI-generated code
If test coverage doesn’t scale with development velocity, every release becomes a risk.
⚠ Manual regression bottlenecks
→ Automated agentic QA coverage
⚠ Incomplete test visibility
→ End-to-end traceability
⚠ Code generation outpaces validation
→ Security integrated into testing
→ Functional & regression testing
Automated test suites run across unit, integration, and end-to-end layers on every build. Agentic QA generates and maintains test cases alongside evolving application code to keep coverage continuously up to date.
Xray for Jira Agentic QA JUnit / pytest Cypress Testrail
→ Security & penetration testing
Dynamic security scans simulate real-world attack scenarios against running applications, while container images and dependencies are scanned for vulnerabilities before deployment. Findings flow directly into Jira for remediation tracking.
Snyk GitLab DAST JFrog Xray SonarQube
→ Performance & compliance testing
Load, stress, and compliance testing establish release readiness before deployment. Regulatory and policy checks — including GDPR, SOC2, and sector-specific requirements – are documented and tracked as part of the delivery workflow.
k6 Confluence Jira Service Management
Release
Every release is signed, versioned, and traceable
Compliance teams need auditability. Delivery teams need speed. The release process should provide both.
⚠ Manual release coordination
→ Automated release gates
⚠ Missing audit visibility
→ End-to-end traceability
⚠ Environment configuration drift
→ Immutable artifact management
→ Release orchestration & approval gates
Automated release pipelines enforce approval workflows across development, security, and change management before artifacts are promoted. Integrated notifications keep all stakeholders aligned throughout the release lifecycle.
Jira Atlassian Confluence Slack Microsoft Teams GitLab Releases
→ Artefact publishing & signing
Containers, libraries, and deployment packages are published with cryptographic signatures and validated against policy controls before promotion into production-ready repositories.
JFrog Artifactory JFrog Xray GitLab Package Registry GitHub Packages
→ Release notes & change documentation
Release documentation is generated automatically from Jira issues, Git activity, deployment metadata, and test results, creating a complete release history linked to associated security and compliance findings.
Confluence Jira Claude Code Rovo Dev
Deploy
Deploy without fear. Roll back in seconds.
Environment inconsistency and manual deployments are where operational risk starts.
⚠ Environment drift across stages
→ Infrastructure-as-code consistency
⚠ Manual deployment processes
→ Zero-downtime delivery
⚠ Slow or risky rollback procedures
→ Instant recovery capability
→ Container orchestration & infrastructure as code
Kubernetes orchestrates container workloads across cloud and on-premise environments, while Terraform and Ansible define reproducible infrastructure configurations. Drift detection identifies unintended changes before they impact production systems.
Kubernetes Docker Ansible AWS Azure
→ Progressive delivery & feature flags
Blue-green and canary deployment strategies reduce release risk by limiting exposure during rollout. Feature flags separate deployment from activation, enabling controlled launches and rollback without rebuilding or redeploying applications.
GitLab Environments GitHub Actions AWS CodeDeploy Azure DevOps
→ Post-deploy validation & smoke testing
Automated smoke tests and health checks validate critical application paths immediately after deployment. Traffic routing only proceeds once readiness conditions are confirmed, helping prevent environment-specific failures from reaching users.
Datadog Synthetics Grafana NordStellar Jira Service Management
Operate & Monitor
Production signals become the next sprint’s requirements
Monitoring without a feedback loop is just expensive logging.
⚠ Alert fatigue
→ Context-aware triage
⚠ Slow incident response
→ Full operational visibility
⚠ No feedback loop to development
→ Closed-loop remediation tracking
→ Infrastructure & application observability
Unified dashboards bring together metrics, logs, and distributed traces across services and environments. Anomaly detection highlights degradation early, while contextual alerting helps teams focus on what needs action.
Datadog Grafana Prometheus AWS CloudWatch Azure Monitor
→ Security operations & threat monitoring
Attack surface management identifies exposed assets, misconfigurations, and runtime security anomalies across cloud and on-premise systems. Findings are routed into Jira Service Management for tracked remediation.
NordStellar Datadog Security Grafana Atlassian Compass Jira Service Management
→ Incident management & feedback loop
Incidents are enriched automatically with observability and security context. Post-incident reviews in Confluence generate Jira follow-up tasks that flow back into planning, turning production insight into delivery priorities.
Jira Service Management Confluence Slack Microsoft Teams Compass
One pipeline. Every tool your team already uses.
We integrate with GitLab, GitHub, Jira, Confluence, AWS, Azure, and the leading AI coding assistants — connecting them into a coherent, secure, AI-native workflow. Whether you're modernizing a legacy system or building greenfield, our Open DevSecOps model meets you where you are.
Drop us a message and we will come back to you as soon as possible.
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