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Secure SDLC & DevSecOps

Embedding Security Across the Software Lifecycle: Faster Releases, Fewer Vulnerabilities, Stronger Outcomes

The economics of secure software development have flipped. Organizations that integrate security across the SDLC ship 1.5x faster than those that don't, detect 75% more vulnerabilities, and reach market 30% sooner, because rework, late-stage remediation, and security-driven release delays disappear. Secure software is no longer a tax on velocity. It is the way to achieve velocity.


The pressure to get this right is rising on three fronts:

 

  • Federal regulation now requires software producers to attest to secure development practices and produce a Software Bill of Materials (SBOM) for software sold into government.
     

  • Cybersecurity risk in outsourced and third-party development is rising sharply: 60% of organizations that outsource development experience a security breach in the first year, and 74% cite loss of cybersecurity control as a significant concern.
     

  • AI-generated code and open-source dependencies introduce vulnerabilities at scale: roughly 1 in 3 AI-generated code snippets contain a security vulnerability, and 84% of published open-source software contain vulnerabilities (48% are considered high-risk).


Our Secure SDLC & DevSecOps service helps you address all of it. Anchored in TBDCyber's NorthStar Vision framework:  Engineering Discipline, Risk Reduction, Operational Efficiency, and AI Trust & Capability Uplift. We help organizations design and operationalize a Product Security program that fits their development culture, aligns with the standards regulators expect (NIST SSDF, NIST CSF, OWASP Top 10, FedRAMP Moderate, CISA Secure by Design), and delivers measurable outcomes against the threats teams actually face.

 

We don't just write strategy documents. We run pilots, train Security Champions, build playbooks your developers actually use, and stay engaged through the operationalization that matters most.

Our North Star Vision Approach Includes
Engineering Discipline

  • Secure SDLC and DevSecOps program strategy aligned to your development culture, technology stack, and regulatory profile.
     

  • Framework alignment: NIST Secure Software Development Framework (SSDF SP 800-218), NIST CSF, OWASP Top 10, OWASP ASVS / SAMM, CISA Secure by Design, FedRAMP Moderate (where relevant), and sector-specific requirements such as FDA cybersecurity guidance for medical devices.
     

  • Secure-by-design and privacy-by-default principles embedded into engineering workflows.
     

  • Security Champions program design and rollout, including the comprehensive Security Champions Playbook and hands-on developer training.
     

  • User Story Threat Modeling is integrated into agile sprints, so it occurs at the development cadence.
     

  • NorthStar Vision development: a multi-year Product Security strategy with foundational pillars, governance, and milestones aligned to your business.

Risk Reduction

  • Threat modeling integration that produces actionable outputs (e.g., unit tests, WAF rules, IR playbooks, secure coding guidelines).
     

  • Software supply chain security: Software Bill of Materials (SBOM) generation and management, SLSA framework alignment, dependency risk analysis, signing and attestation strategy, and provenance controls. 
     

  • Secure code scanning and testing strategy: SAST, DAST, SCA, IAST, secret scanning, and infrastructure-as-code scanning.
     

  • Secrets management strategy across applications, pipelines, and machine identities, with cross-reference to identity infrastructure.
     

  • Vulnerability management and remediation prioritization in a development context, fixing the right things at the right cadence.
     

  • Open-source risk management: dependency analysis, license compliance, vulnerability triage, and the realities of consuming code with vulnerability exposure.

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Operational Efficiency

  • DevSecOps tooling strategy across the pipeline: vendor-neutral evaluation of SAST, DAST, SCA, secrets scanners, container scanners, IaC scanners, and the platforms that integrate them.
     

  • CI/CD pipeline security architecture and integration design.
     

  • Security-as-code patterns for repeatable, version-controlled, automated security.
     

  • Integration with ticketing, ITSM, and developer workflow tools, so security findings show up where developers actually work.
     

  • Platform engineering security: integrating security into Internal Developer Platforms (IDPs) and "paved road" patterns.
     

  • Metrics, OKRs, and tactical measurement that align security activity with engineering outcomes, not just compliance checkboxes.

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AI Trust & Capability Uplift

  • AI-powered security tooling evaluation: AI-augmented SAST, DAST, automated threat modeling, intelligent vulnerability prioritization, and emerging AI-native security tools.
     

  • AI coding assistant security for development teams: policy, usage guidelines, prompt hygiene, IP protection, secret leakage prevention, and validation patterns for AI-suggested code.
     

  • AI Trust & Security Roadmap development: AI-specific use cases, governance integration, Responsible AI (RAI) practices, and AI tooling implementation strategy.
     

  • Customized training programs covering Security Champions, Threat Modeling, Secure Code Scanning, and AI-aware secure development.



     

Benefits

1.5x Faster Delivery, 30% Faster Time to Market

Integrating secure software design across the SDLC eliminates rework cycles and late-stage remediation that quietly consume engineering capacity. Speed and security move in the same direction, not opposite directions.

Risk Reduction Across the Full Surface, Including Supply Chain

Threat modeling is integrated into sprints. SBOMs that satisfy regulators. Open-source and AI-generated code risk is addressed at the development layer, where it can actually be fixed. The supply chain attacks that defined the past five years are designed against.

Operational Efficiency Through Automation and Standardization

DevSecOps practices that scale across teams, automate repetitive security work, and integrate with developer workflows, so security activities add throughput rather than remove it.

AI Trust and Capability Uplift That Compounds

AI tools in development workflows are used responsibly, AI capabilities are embedded in security tooling, and training that lifts your engineering team's security capability for the long term, not a one-time project.

Related Services

Secure SDLC & DevSecOps is the program-level engagement. For deeper dives in specific areas:

 

Threat Modeling for methodology-led threat modeling engagements.

 

Application Security Testing for AppSec testing of web, mobile, API, and AI-enabled applications;

 

Secure AI & Agentic Development for the specific challenges of building AI-powered products and AI agents securely.

 

For software supply chain risk from the third-party vendor angle, see Third-Party Risk Management.

 

For the broader architectural anchor, see Zero Trust Architecture Strategy.

 

For the cloud and AI infrastructure your applications run on, see Cloud & AI Workload Security Architecture.

Secure Software Development Is Faster, Not Slower — When It's Done Right.

Organizations that integrate security across the SDLC ship faster, detect more vulnerabilities, and reach market sooner than those that bolt security on at the end. The economics have flipped, and the pressure from regulators, AI-generated code risk, and software supply chain attacks is making the choice mandatory.


TBDCyber helps you operationalize Secure SDLC and DevSecOps anchored in the four NorthStar Vision pillars: Engineering Discipline, Risk Reduction, Operational Efficiency, and AI Trust & Capability Uplift. Vendor-neutral, framework-aligned, and proven across regulated industries from medical devices to financial services.


Talk to a Secure SDLC Expert →

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