8 SDLC Security Best Practices to Mitigate Software Vulnerabilities
Traditional manual SDLC security practices are often slow and inconsistent. AI agents like those offered by Jit are an effective way to streamline and automate many phases of the SDLC, helping teams implement design-level security to reduce architectural flaws and boosting the effectiveness of security testing phases.
Published August 6, 2025
In today's interconnected digital landscape, software applications are the backbone of almost every industry. From critical infrastructure to daily consumer interactions, the reliability and security of these applications are paramount. However, the rapid pace of development often leads to security being an afterthought, leading to costly and devastating consequences.
According to IBM's 2024 Cost of a Data Breach Report, the global average cost of a data breach reached a staggering $4.88 million. This figure underscores the immense financial burden, reputational damage, and legal repercussions that organizations face when vulnerabilities are exploited. Integrating security throughout the software development lifecycle (SDLC) is no longer optional; it's a fundamental requirement for building resilient and trustworthy software.
» Learn how Jit's AI agents can boost software security
Critical Limitations of Traditional Manual SDLC Security Practices
- Delayed feedback and slow remediation: Manual code reviews or periodic audits surface vulnerabilities late in the development cycle, which delays releases and makes fixes more costly and complex as developers must revisit old code with lost context. The longer issues linger, the higher the chance they slip into production.
- Inconsistent policy enforcement: Without automated checks, different teams may interpret and apply security standards inconsistently. This leads to uneven coverage, increases the likelihood of vulnerabilities passing through unnoticed, and makes compliance audits more difficult without uniform traceability.
- Lack of real-time visibility: Manual processes often rely on spreadsheets, emails, or fragmented tools that lack real-time status or centralized dashboards. This prevents security and engineering leads from quickly identifying risk trends, team performance, or the most critical unresolved issues.
- Developer friction and low adoption: Manual security reviews can feel like top-down interference, especially when they're last-minute or not embedded in the dev workflow. This causes pushback from developers and may result in bypassed processes, workarounds, or minimal security engagement.
- Bottlenecks in release cycles: Manual sign-offs and security gates often delay deployments, particularly when teams are waiting on overburdened security reviewers. As teams scale, these delays multiply and can cripple CI/CD velocity.
- Difficulty scaling security expertise: Manual approaches depend heavily on a small number of security experts who must be involved in every stage. As teams grow, these experts become overwhelmed, creating knowledge bottlenecks and gaps in coverage that automated tools could help bridge.
» Here are the best code security tools for 2025
8 Best Practices for Securing Each Phase of the SDLC
1. Streamline and Automate With AI Agents
Integrating AI agents into your SDLC can revolutionize how you approach security. These intelligent tools go beyond traditional automation, offering advanced capabilities that significantly enhance vulnerability detection, remediation, and overall compliance.
By offloading repetitive and complex tasks to AI, development teams can focus on innovation while building more secure software from the ground up.
Here's how AI agents can streamline and automate your SDLC security:
- Automated vulnerability detection and prioritization: AI agents can continuously scan your codebase, identifying security flaws with greater accuracy and fewer false positives than traditional methods. They prioritize these vulnerabilities based on real-time risk context that leads to faster remediation cycles and more efficient allocation of security resources.
- Intelligent issue triage and assignment: AI can automatically classify security findings and route them to the appropriate developers or teams based on their expertise and code ownership. This accelerates response times, improves accountability, and ultimately boosts resolution rates.
- Automated fix suggestions and code remediation: AI agents can suggest or even apply fixes automatically, proposing secure code changes directly within pull requests. This not only speeds up the remediation process but also helps maintain coding standards, reducing developer workload and fostering more secure coding practices.
- Continuous compliance monitoring: Maintaining compliance can be a significant burden, but AI agents can continuously analyze your code and configurations against various compliance standards to ensure you're always audit-ready without constant manual checks.
- Security awareness and training: AI can play a crucial role in fostering a proactive security culture. By analyzing coding patterns, AI agents can deliver personalized, context-aware security tips and training directly to developers, helping them understand and avoid common pitfalls.
» Worried about cyber attacks? Here are the best ways to secure AI agents from cyber attacks
Automatic Vulnerability Prioritization
Jit's AI Agents consider all the factors of risk prioritization from active exploits to compensating controls to automate the process at scale.
Learn More2. Lay the Foundation With Early Planning
Effective security begins through early, intentional planning with the following procedures:
- Conduct thorough risk assessments: This identifies potential threats, vulnerabilities, and impacts, enabling teams to prioritize security controls aligned with business goals.
- Establish a clear security budget: This ensures dedicated resources for tools, training, and incident response, preventing security from becoming an afterthought.
- Define explicit security roles and shared responsibilities: Defining security champions embedded in dev teams or dedicated security engineers with specific responsibilities creates accountability and smooth collaboration.
- Integrate threat modeling: This helps anticipate attack vectors specific to the application architecture.
» Not sure where to start? Understand the principles of risk management in software security
How Automation Helps
Tools like Jit enhance this phase by automating security policy enforcement and continuous risk monitoring, giving teams visibility into security posture from day one.
It helps embed security requirements into the development workflow, reducing manual overhead and aligning budgets and roles with real-time security insights, laying a solid foundation for secure software development.
3. Gather and Document Security Requirements
Teams should start by collaborating with stakeholders—including legal, compliance, product, and security—to identify relevant compliance standards (GDPR, HIPAA, etc.), user access controls, and data protection needs early in the project.
Conducting workshops or threat modeling sessions helps uncover nuanced security requirements specific to the business context, and documenting these clearly in a security requirements specification (SRS) ensures alignment across teams while guiding development.
Using standardized templates like OWASP’s Top 10 or NIST frameworks streamlines capturing critical controls and reduces omissions.
How Automation Helps
Tools like Jit complement this by embedding compliance checks and access policies directly into the development workflow, automating enforcement and real-time validation. This integration prevents drift from security requirements throughout the development lifecycle and keeps teams accountable to defined standards without slowing delivery.
4. Implement Design-Level Security to Reduce Architectural Flaws
The most effective way to reduce architectural flaws is for teams to prioritize threat modeling early, which is systematically identifying potential attack vectors and vulnerabilities, as this helps focus security efforts on the highest-risk areas.
Applying secure architecture patterns like least privilege, defense in depth, and secure defaults prevents common design mistakes and limits damage from breaches. Additionally, data flow analysis reveals how sensitive information moves through the system, highlighting exposure points for interception or leakage.
How Automation Helps
Tools like Jit enhance this phase by automating threat detection and enforcing architectural policies during code reviews, making secure design easier to maintain and verify. By integrating these checks early, teams reduce costly rework and deliver systems built with security as a core principle.
5. Enforce Secure Coding Standards and Developer Training
Adopting clear, language-specific secure coding standards helps developers avoid common pitfalls like injection flaws, improper error handling, or insecure API use.
Ensure the following practices:
- Regular updates: Standards should be regularly updated and accessible to all employees.
- Continuous developer training: This includes hands-on workshops and real-world examples to reinforce secure habits and raise awareness of evolving threats.
- Integrate secure code reviews as a mandatory step: This ensures peer validation of security practices to catch issues early.
Together, these practices create a feedback loop that fosters a security-first mindset, reduces human error, and accelerates the delivery of robust, secure software.
How Automation Helps
Automated tools like Jit can augment reviews by scanning code for vulnerabilities and enforcing coding policies directly within pull requests. Using version control systems with branch protection and automated scanning pipelines embeds security checks seamlessly into the workflow, preventing insecure code from merging.
» Here are our picks for the best API security tools in 2025
6. Maximize the Potential of Security Testing
During the testing phase, teams should employ a layered approach with the following strategies:
- SAST (static application security testing) for early detection of code-level vulnerabilities
- DAST (dynamic application security testing) to assess runtime behavior and identify configuration or injection flaws
- Penetration testing for deep, manual exploration of complex attack vectors
- Security regression testing to ensure fixes don’t break or reintroduce vulnerabilities
Issues should be prioritized based on severity, exploitability, and business impact using risk scoring frameworks like CVSS.
» Learn more: Best SAST tools and best DAST tools
How Automation Helps
Integrating Jit streamlines issue tracking by automatically categorizing vulnerabilities, linking them to relevant code changes, and providing actionable remediation guidance within developer workflows. This reduces alert fatigue and helps teams focus on high-risk flaws first, ensuring continuous security improvement without blocking release velocity.
7. Optimize Deployment Security
To ensure only safe code reaches production, teams must implement secure build pipelines that automate security scans and enforce policy gates before deployment. This includes:
- Container hardening: Including minimal base images, vulnerability scanning, and runtime protection reduces attack surfaces in containerized environments.
- Validating infrastructure-as-code (IaC) templates against security benchmarks: This prevents misconfigurations that could expose resources.
How Automation Helps
Jit supports these practices by integrating automated security checks directly into CI/CD pipelines, continuously validating code, containers, and IaC security before production rollout. It enforces policy compliance, blocks unsafe deployments, and provides real-time alerts for any violations, helping teams maintain strong security hygiene without slowing down releases.
» Don't miss the best IaC security tools
8. Don't Forget About Post-Release Ongoing Monitoring
Post-release, continuous security monitoring is crucial to detect anomalies, intrusion attempts, or new vulnerabilities in production environments. Leveraging tools that aggregate logs, alerts, and threat intelligence enables rapid identification of risks.
Regular patch management even after production ensures timely updates of software dependencies, operating systems, and infrastructure components, closing known security gaps before exploitation.
A well-defined incident response plan—including clear roles, communication channels, and playbooks—allows teams to swiftly contain and remediate breaches, minimizing damage and downtime.
» Here are our tips to prevent dependency confusion attacks
How Automation Helps
Platforms like Jit enhance these strategies by providing automated vulnerability detection, prioritized remediation workflows, and integration with monitoring tools. This continuous feedback loop helps teams stay proactive, maintain compliance, and improve security posture throughout the software lifecycle.
SDLC Security FAQs
What emerging technologies are expected to escalate existing software vulnerabilities?
- Generative AI: Generative AI can accelerate the creation of highly sophisticated malware and enable adversarial attacks against AI-powered security systems, while vulnerabilities in its training data could inject flaws directly into the software supply chain.
- Quantum computing: The primary concern with quantum computing is its ability to break current cryptographic standards, potentially compromising encrypted data and digital signatures, necessitating a rapid shift to post-quantum cryptography.
- IoT expansion: The vast proliferation of IoT devices drastically expands the attack surface, often featuring weak default security, insecure update mechanisms, and outdated components that make them ripe targets for botnets and data breaches.
- Low-code platforms: Low-code platforms can foster "shadow IT" and allow citizen developers to inadvertently introduce vulnerabilities due to a lack of security expertise, while the abstraction of code can limit crucial security oversight and customization.
How should secure SDLC practices evolve to address these new risks proactively?
- Embrace continuous AI-powered security automation: Integrate AI agents like Jit to automate vulnerability detection, triage, and remediation across all SDLC phases. This proactive automation reduces manual effort, accelerates fixes, and adapts to emerging threats in real time, preventing security gaps caused by evolving AI-generated or low-code environments.
- Update security policies for low-code and AI code generation: Revise secure coding standards and threat models to specifically include risks from AI-generated code and low-code platforms. Train teams on new attack surfaces and establish automated policy enforcement to catch unconventional vulnerabilities early.
- Foster cross-functional collaboration and shared responsibility: Encourage ongoing communication between development, security, and operations teams to quickly identify and mitigate risks introduced by infrastructure-as-code or AI workflows. Use integrated platforms like Jit to centralize visibility and streamline secure decision-making.
How can organizations audit their current SDLC practices to assess security maturity?
Organizations can audit their SDLC security maturity by conducting comprehensive assessments that review policies, tool usage, developer training, and incident response effectiveness. This includes mapping security controls against industry frameworks (like NIST or OWASP), analyzing vulnerability trends, and gathering developer feedback. Automated tools like Jit can help generate real-time reports on security posture and compliance gaps.
What red flags should prompt an immediate overhaul?
Red flags prompting immediate overhaul include:
- Frequent security incidents
- Lack of automated testing in pipelines
- Poor vulnerability remediation times
- Inconsistent policy enforcement
- Minimal developer security awareness
These signs indicate fragile processes that put the organization at high risk and require urgent, structured improvements.
» Learn more: Key categories and tools for SDLC security
From Reactive to Proactive: The Dawn of Secure SDLC
The digital landscape is constantly evolving, with new technologies introducing both incredible opportunities and significant security challenges. In this dynamic environment, relying on traditional, siloed security checks is no longer sufficient. The path to truly resilient software lies in embedding security as an intrinsic part of every stage of the SDLC, transforming it from an afterthought into a foundational element.
The cornerstone of this proactive approach is the strategic adoption of automated, continuous monitoring tools. These aren't just an enhancement; they are a necessity. By providing real-time visibility into your codebase, flagging vulnerabilities as they emerge, and even suggesting remediations, these tools empower development teams to build security in, rather than bolt it on.
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