Secure SDLC Governance
End-to-end assessment of your software development lifecycle — design security, threat modelling, code review controls, testing adequacy, and deployment gate effectiveness.
Independent assurance over how your software is built — not just deployed. Including the governance risks of AI-assisted development in regulated financial environments that most security programmes have not yet addressed.
The challenge
Most breaches exploit application-layer vulnerabilities. Security reviews consistently happen too late in the development cycle — after risk is already embedded in production code.
Application security reviews and penetration tests typically occur near or after deployment — when the cost of remediation is highest and the window for structural improvement has closed. Assurance over the process that builds software is more effective and more cost-efficient than testing the output after the fact.
Developers using AI coding assistants introduce new vulnerability classes, intellectual property risks, and governance gaps into regulated codebases. Few financial institutions have frameworks for AI-assisted code that meet the expectations of regulators, internal audit functions, or operational risk committees.
Engagement models
EONTA delivers secure code assurance across two structured tracks — SDLC governance and AI code risk — each calibrated to your development environment and regulatory context.
Structured evaluation of your secure software development lifecycle — design, development, testing, and deployment controls — assessed against OWASP SAMM maturity levels and MITRE ATT&CK technique coverage.
Process assurance — not just code scanning
OWASP + MITRE dual-framework approach
DevSecOps controls assessed as standard
Independent evaluation of your AI coding assistant governance — how AI-generated and AI-assisted code is reviewed, tested, and controlled across your regulated development environment.
AI-assisted code governance included as standard
LLM-specific risk framework applied
Regulatory context built in throughout
Core capabilities
Each capability evaluates the process that produces software — not just the output — applying OWASP, MITRE, and AI-specific governance frameworks throughout.
End-to-end assessment of your software development lifecycle — design security, threat modelling, code review controls, testing adequacy, and deployment gate effectiveness.
Structured evaluation of your Software Assurance Maturity Model level across all five business functions — governance, design, implementation, verification, and operations.
Adversary technique mapping against your application security controls — identifying where known attack techniques would succeed against your current development and deployment environment.
Independent evaluation of Copilot, ChatGPT, and other LLM-based coding tool governance — policy, review workflow, data handling, and compliance controls in regulated environments.
Pipeline security assessment — build tool integrity, secret management, container security, infrastructure-as-code controls, and deployment gate effectiveness across your CI/CD environment.
SBOM validation, licence compliance, known vulnerability assessment, and third-party component governance across your application dependency landscape.
How it works
A structured engagement calibrated to your development environment, tech stack, and regulatory requirements — minimising disruption to engineering teams while delivering audit-grade findings.
Map development environment, technology stack, AI coding tool usage, and SDLC process boundaries. Define assessment focus areas.
Evaluate SDLC governance controls, OWASP SAMM maturity, AI code tool usage, and DevSecOps pipeline security.
Apply MITRE ATT&CK technique mapping, dependency risk review, and targeted control effectiveness testing across assessed areas.
Structured findings — SDLC gaps, AI governance deficiencies, OWASP SAMM maturity score, and remediation roadmap.
Why EONTA
Automated code scanning finds known vulnerabilities in existing code. EONTA assesses the process that produces code — whether secure development practices are embedded early enough to prevent vulnerabilities from being written in the first place. Process-level assurance is more durable and more cost-effective.
AI coding assistant governance is not an optional add-on in our assessments — it is a standard component, because the regulatory and security risks it introduces are already material and auditable. Most security assessments were designed before AI-assisted coding became standard practice.
Our methodology applies both OWASP SAMM maturity scoring and MITRE ATT&CK technique coverage simultaneously — providing a governance maturity view and a threat-realism view of the same development environment. Each alone is insufficient; together they produce actionable, evidence-grade findings.
Who this is for
EONTA's SCDA services are designed for the governance functions, technical leadership, and risk roles accountable for application security quality, regulatory standing, and development risk.
Primary stakeholders
Common engagement triggers
New AI coding tool adoption or LLM integration
Engineering teams adopting Copilot, ChatGPT, or other LLM-based coding tools in regulated environments requiring governance framework validation before broader rollout.
Regulatory audit, penetration test finding, or security incident
Organisations requiring independent SDLC assurance following an application security audit finding, penetration test result, or security incident traced to development process gaps.
SDLC transformation, DevSecOps implementation, or platform migration
Institutions undergoing significant development environment change requiring independent validation that security controls are embedded correctly in the new process.
Frequently asked
Take the next step
Most regulated institutions don't have a documented answer. An SCDA scoping call takes 45 minutes and produces a clear picture of your exposure.
All scoping conversations are confidential. EONTA does not share engagement details with third parties.