AI-generated code fails security testing on nearly half of all tasks, according to Veracode’s latest testing, and catching that before it makes it into production is what a secure software development lifecycle (SDLC) is built to do.
Many teams still see security as something to do at the end of the development process; however, a secure SDLC builds security into every phase of development, so vulnerabilities get caught earlier. With AI, code now ships faster, so new approaches to the software lifecycle are needed. The principles of a secure SDLC have not changed, but AI-assisted development is changing how quickly teams need to apply them.
What Is a Secure SDLC?
When creating software, a secure SDLC is a framework that embeds security through all stages, including requirements, design, coding, testing, and deployment. The goal is to catch and fix issues early, when changes cost far less than they do after code reaches production.
There are three main principles to this approach:
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Shift-left security: Moving security activities earlier into requirements and design phases, rather than waiting until testing.
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Continuous validation: Running security checks throughout development, not just once before release.
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Secure design: Building secure-by-design principles into architecture decisions from the beginning.
Traditional SDLC vs Secure SDLC
In a traditional SDLC, teams move through each stage, with security often appearing only at the end. A penetration test or security review happens right before launch, and any findings create a last-minute rush or get pushed to a future release.
Secure SDLC integrates security practices into every single phase from start to finish. This is still important for AI-generated code, because AI tools can now write code faster than any team can manually review it. A secure SDLC should catch issues at every commit.
The Importance of a Secure SDLC for Software Delivery
Aside from reducing vulnerabilities, a secure SDLC also supports compliance efforts, with frameworks such as SOC 2, PCI DSS, and HIPAA requiring documented evidence of secure development processes.
A continuous approach to security also makes audits less stressful. When security activities produce evidence as part of everyday development, teams can show continuously collected artifacts such as threat models, code review records, and automated scan results.
There’s also the cost factor, as vulnerabilities discovered after deployment cost much more to fix than issues caught during design. IBM’s 2025 Cost of a Data Breach Report found that a DevSecOps approach reduced average breach costs by $227,192. A secure SDLC catches problems when changes are less expensive.
Stages of the Secure Software Development Lifecycle
To build security into the development process, it’s important to understand the role each phase plays. A secure SDLC typically consists of six stages, each supported by specific security activities and controls.
1. Secure Requirements
In this first step, teams should set out security requirements together with functional ones. It includes deciding how sensitive data should be managed, what authentication and authorization controls are needed, and which regulatory or compliance requirements the application must meet.
Getting these requirements in place early gives architects and developers clear security goals to build against, reducing the chances of expensive changes later in the project.
2. Threat Modeling at Design
After the requirements are defined, the next step is to examine the design from an attacker’s perspective. Threat modeling helps teams identify potential threats, attack paths, and the security controls needed to reduce risk before any code is written.
For example, if an application allows file uploads, it’s good to question how an attacker could abuse that feature. Or what about if users can reset their passwords? What opportunities does that create for account takeover? Frameworks such as STRIDE provide a structured way to work through these questions, while MITRE ATLAS catalogs real-world adversarial tactics and techniques specific to AI and agentic systems.
The result of threat modeling is a documented set of threats, risk ratings, and recommended security controls that guide development and testing. The challenge is that traditional threat modeling can be slow and difficult to scale, which is why many teams are turning to automated solutions.
3. Secure Coding Standards During Development
Next are secure coding standards for development practices, which give developers guidance on how to build features securely and cover areas such as input validation, output encoding, authentication, and session management.
The OWASP Secure Coding Practices guide is a useful start and has recommendations like:
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Input validation: Check that data matches expected formats before processing it.
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Parameterized queries: Keep code and user input separate to prevent SQL injection attacks.
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Secrets management: Store sensitive information in secure vaults instead of hardcoding credentials directly in code.
However, developers also need practical experience applying these principles to realistic scenarios. With AI-generated code becoming part of the development process, they also need to know how to review AI suggestions, prompt securely, and decide what is safe to use. Hands-on training in development environments helps teams build these skills into their normal workflow.
4. Security Testing and Code Review
When teams need to verify that security controls have been implemented correctly before software reaches users, that’s where testing comes in. At this stage, teams use multiple approaches to identify vulnerabilities, including Static Application Security Testing (SAST) to analyze source code, Dynamic Application Security Testing (DAST) to test running applications, and Software Composition Analysis (SCA) to detect vulnerable dependencies.
Then there are AI-assisted scanning tools, such as Claude Security, that can also reason about code rather than just matching known patterns, catching context-dependent issues that traditional SAST tools miss. Findings still go to a developer for review and approval, so recognizing a vulnerability and fixing it correctly remain primary skills.
Another important point is that these tools need to be integrated into CI/CD pipelines so there’s automated feedback on every commit. Also, peer code review with a security checklist allows developers to catch logic flaws that automated scanners struggle to detect.
5. Secure Deployment
Moving code into production has its own security risks. During deployment, teams need to secure the environments where applications run by configuring access controls, encrypting secrets, signing artifacts, and checking Infrastructure as Code templates for security issues.
6. Monitoring and Maintenance
The last stage is when teams need to monitor applications for suspicious activity, maintain proper logging and alerting processes, and also patch and remediate any new vulnerabilities that get through.
Secure SDLC Frameworks and Standards
Organizations don’t have to start from scratch, but can use frameworks and standards that are already established to guide their secure SDLC practices.
OWASP SAMM and ASVS
Considering organizations all differ, there isn’t a single way to implement a secure SDLC. They often use established frameworks and standards to understand their current security maturity, define best practices, and meet industry or compliance requirements. These include OWASP’s Software Assurance Maturity Model (SAMM) and the Application Security Verification Standard (ASVS).
Recently, OWASP released another standard for AI systems, known as AISVS. It follows the same approach as ASVS, but covers AI-specific areas such as model lifecycle management, supply chain risk, and security controls for agents and MCP.
NIST Secure Software Development Framework
Then there is the NIST Secure Software Development Framework (SSDF), which provides guidance for building security into the SDLC. Its practices are organized into four areas: preparing the organization, protecting software, producing well-secured software, and responding to vulnerabilities.
Many organizations use SSDF as a reference point for meeting security expectations in areas such as government contracts and critical infrastructure.
There is also NIST SP 800-218A, which extends the SSDF specifically for AI and machine learning systems, focusing on practices for securing training data, models, and AI-generated code.
SLSA and Supply Chain Provenance
Today’s applications use a lot of third-party components, making software supply chain security an important part of the SDLC. The Supply Chain Levels for Software Artifacts (SLSA) provides a framework for improving build integrity and verifying where software comes from.
The same approach extends to AI components through an AI Bill of Materials (AIBOM), which includes inventory models and datasets rather than just code dependencies.
Risks and Vulnerabilities Across the SDLC
Understanding the issues that can appear throughout development makes it easier to focus security efforts where they have the most impact.
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Insecure coding: Injection flaws, cross-site scripting (XSS), and broken access control often come from insecure development practices and remain among the most common application security risks.
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Dependency vulnerabilities: Outdated or compromised dependencies can introduce security issues, with ReversingLabs reporting that malicious open source packages increased by 73% last year.
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Misconfigurations: When there are incorrect settings, excessive permissions, exposed secrets, and unpatched infrastructure, they can create unnecessary attack paths that attackers can exploit.
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Supply chain attacks: According to the Verizon 2025 DBIR, third-party involvement in breaches doubled to 30%, showing the importance of securing the broader software supply chain.
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Insufficient logging: If there’s not enough visibility into application activity, organizations can find it difficult to detect and respond to security incidents quickly.
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AI-generated vulnerabilities: Missing input validation, weak error handling, and other patterns that get through when code ships faster than it’s reviewed.
Adapting the Secure SDLC for AI-Assisted Development
These days, as features ship much more quickly with AI, manual threat modeling becomes a bottleneck, and code review capacity can’t match the rapid pace of code generation.
The secure SDLC framework itself doesn’t need to change, but how teams implement it does, as there needs to be more automation and new skills for reviewing and validating what AI writes.
Threat Modeling to Match AI-Driven Development Speed
When it comes to threat modeling for AI systems, automated approaches are the way to go. They generate and update threat models quickly, connecting identified risks with workflows such as Jira and Azure DevOps, so threat modeling keeps pace as architectures change.
Automated threat modeling solutions, such as ThreatCanvas, use this approach by generating threat models from sources including text descriptions, diagrams, Infrastructure as Code templates, and code repositories. Threat models can then continue to support decision-making as systems change.
Secure Coding Training for AI-Generated Code
Developers need to understand the security implications of AI-generated code before accepting it into the codebase.
Training now needs to cover areas such as:
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Secure prompting: Reducing the likelihood of insecure AI-generated output.
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AI code review: Reviewing generated code for vulnerabilities and insecure patterns.
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Security tool-assisted review: Combining SAST and SCA feedback with human judgment, since automated tools can identify many issues but may miss context-dependent vulnerabilities.
Securing the New Attack Surface of Agentic AI Skills and MCP
Agentic AI adds new components to the software process. Autonomous agents that call tools, interact with APIs, and make decisions across multi-step workflows introduce new security risks that need to be addressed throughout the SDLC.
Threat Modeling Agents and Tool Use
Like any other software component, AI agents need to be threat modeled. Teams need to consider risks such as prompt injection, excessive permissions, insecure tool access, and unvalidated outputs from agent-invoked systems.
Securing MCP Integrations and Skills
As agents gain more capabilities, teams need to carefully control what they can access and execute. Model Context Protocol (MCP) is an open standard that allows AI systems to connect with external tools, data sources, and services, making it easier for agents to perform more complex tasks.
Securing MCP integrations and agent skills requires many of the same principles used in traditional application security, including limiting permissions, validating inputs, and checking outputs before they are trusted.
Training Developers to Build Secure Agents
Developers need practical experience with the security challenges introduced by agentic applications. Guidance from resources such as the OWASP Top 10 lists for LLMs and agentic application security, combined with hands-on practice, gives teams the necessary skills to build more secure AI systems.
How SecureFlag Supports a Continuous Secure SDLC
AI-assisted development doesn’t replace the need for good security practices, but it does add new ones. A secure SDLC needs security throughout development, new developer skills, and automated feedback that can keep pace with the speed of delivery.
SecureFlag prepares developers to work securely with traditional and AI-generated code through hands-on training, including Agentic Coding Labs for AI-assisted development. This is backed by ThreatCanvas, helping teams identify and address security risks throughout the development process with automated threat modeling.
Overall, SecureFlag helps organizations move toward a more continuous approach to software security, where risks are found earlier, and security becomes part of everyday engineering workflows.
Book a demo to see SecureFlag in action.
FAQs About Secure SDLC
What is the difference between SDLC and SSDLC?
SDLC refers to the standard software development lifecycle, the phases teams follow to build software. SSDLC (Secure SDLC) embeds security activities into every phase, rather than treating security as a final gate before release.
When should threat modeling happen in a secure SDLC?
Threat modeling should be done during the design phase, before code is written, and then updated continuously as the architecture changes. Starting early catches design flaws when changes are less expensive, and continuous updates make sure the threat model is still relevant as systems grow.
How does AI-generated code change secure coding training?
Developers working with AI assistants spend more time reviewing and remediating generated code than writing from scratch. Training programs now address secure prompting, identifying insecure AI suggestions, and fixing vulnerabilities in AI-generated code.
What are the top security risks introduced by agentic AI in the SDLC?
The top risks include prompt injection attacks, excessive permissions granted to agents, unvalidated outputs from agent-invoked APIs, and expanded attack surface from MCP integrations and skills.
How can teams make a secure SDLC continuous?
Teams should automate security testing in CI/CD pipelines, use automated threat modeling that updates with architecture changes, and integrate hands-on training into developer workflows. The goal should be security feedback on every change rather than periodic reviews.
