Artificial Intelligence is transforming software development and the product lifecycle by enabling automated code reviews, adaptive testing, configuration, monitoring, and compliance checks. This results in higher quality software, faster time to market, and more innovative solutions.

However, this rapid transformation also introduces significant challenges related to data security, model protection, and regulatory compliance. As organizations scale their use of AI-powered software, they often overlook a critical aspect: security and compliance.

According to IBM’s Institute for Business Value (IBV), only 24% of AI projects currently include security components. Yet, 82% of respondents believe there is a strong need for reliable and efficient AI-powered security solutions to support business growth. This reveals a critical gap where many businesses remain unaware of the security threats they face.

Traditional threats such as malware and social engineering persist and still require focused attention. Compounding this, many organizations are not equipped to handle emerging AI-related threats due to insufficient security mechanisms and weak compliance practices. Some cyber attackers are already using AI techniques to execute more efficient, targeted attacks in less time.

As AI adoption accelerates, it brings with it a new generation of threats. While these are not yet widespread, they are expected to escalate as the market for AI-driven tools, systems, and applications continues to expand.

This blog explores how AI can impact in securing software development lifecycle.

What is AI-Powered Secure SDLC?

AI- Powered secure SDLC is an integration of Artificial intelligence and machine learning into every stage of software development lifecycle to strengthen security. AI helps in automating code reviews, security testing, and vulnerability scanning. It also assists developers to identify and address security issues at the earliest, which reduces risks and saves lot of time. AI-driven threat modeling and risk assessments offer in-depth insights on potential vulnerabilities, while constantly monitoring and identifying new threats as software evolves. These methodologies also protects AI models and data which ensures security against threats and data breaches.

By integrating AI-powered security into software development workflows, developers can focus on creating innovative applications, without compromising on security. This approach results in more resilient and compliant software systems.

Key Benefits of Using AI in a Secure Software Development Lifecycle

AI offers multiple benefits to secure software development lifecycle workflows. Here’s a breakdown of benefits:

Quicker Threat Detection and Remediation

AI significantly improves the speed of accuracy of threat detection across large codebases. Machine learning models can analyze large amounts of code and configuration files, flag anomalies, and detect threats that conventional security tools fail to identify. Most importantly, AI just does not detect threats and vulnerabilities, it also provides remediation measures to fix. This helps developers in fixing flaws at the early stage and saves a lot time and avoids vulnerability exploitation by attackers.

Provides Real Time Feedback for Developers

One of the core principles of secure SDLC is “shift left in devsecops” approach which integrates security in the early phase of development lifecycle. AI plays an important role by offering real time security directly in developer’s Integrated Development Environment (IDE). As developer write code, AI scans them real time and highlights potential vulnerabilities and then offers remediation insights. This real time support enables developers to write secure code from the early stage and reduces their rework and eliminates delay in later stage audits.

Intelligent Risk Prioritization

Security teams often get overwhelmed with backlog of vulnerabilities, but not all of them pose security risk. AI helps in prioritizing these risks properly by analyzing them in context taking into consideration as to how a vulnerability is exploitable, how critical the affected system is, and potential impact if it is exploited. This prioritization of risk allows teams to focus on the most severe flaws first, optimize remediation workflows and minimize the probability of security breach caused by missed flaws.

Easy Integration with DevOps Workflows

AI-Powered security solutions can be integrated directly into existing DevOps tool stack such as CI/CD pipelines, IDE’s and version control platforms. This deep integration ensure automatic security checks at every stage right from coding to deployment without disrupting development flow. As a result, security becomes an integral part of the process which improves protection without causing any interruption in innovation or delivery deadlines.

Collaboration between development and Security Teams

Historically, there has always been disconnect between development and security teams, due to manual process and misaligned priorities. AI powered security solution helps fix this gap by automating repetitive and routine security tasks by integrating security context directly into development workflow. This approach builds better communication, aligns goals across the teams and encourage developers to take on more responsibilities in terms of security.

The Role of AI in a Secure Software Development Lifecycle

An AI-powered Secure Software Development Life Cycle (SDLC) integrates intelligence, automation and policy enforcement to build resilient defense mechanism into every phase of software development. Here's a breakdown of its role:

Governance for Open Source and Packages

The new generation software is dependent on open source components, which can invite malicious code or hidden vulnerabilities. AI-powered governance frameworks implement strict policy based controls to examine third party dependencies before they are integrated and also enables automatic flagging of non-compliant or risky packages.

Language and Frameworks Coverage

AI generated code spans across different programming languages and frameworks. AI-powered Secure SDLC tools provide complete language and frameworks compatibility to automatically scan, monitor and secure applications across the entire tech stack which ensures there are no gaps.

Contextual Prioritization of Risks

AI improves the security triage by analyzing the severeness of exploitability, exposure and business impact of vulnerabilities. This context aware prioritization ensures that limited resources are toward fixing the most critical issues first , than being overwhelmed by low risk issues.

Enforce Security Policies at Scale

AI-powered policy enforcement ensures consistent security practices are in place across all teams and projects. With centralized oversight security, dev teams can detect policy drift early and maintain compliance through automated alerts and enforcement mechanisms.

Developer Enablement through AI Insights

AI tools not only detect vulnerabilities but also train developers with contextual feedback and code-level suggestions. Paired with hands-on, real-world training simulations, this enables developers to write secure code intuitively which bridges the gap between awareness and action.

Securing the Software Supply Chain

AI models often depend on vast third-party datasets, pre-trained models, and libraries. examining these supply chain components is crucial. AI can monitor provenance, scan for known threats, and flag anomalies before they impact production systems.

CI/CD Security Integration

Integrating AI-powered security into CI/CD pipelines ensure continuous vulnerability detection and remediation. Automated testing and policy enforcement within the pipeline enable rapid, secure releases without compromising speed or agility.

Top AI-Powered Secure SDLC Tools

AI-powered security tools that are specifically designed for software development lifecycle help identify, prevent and fix security flaws right from the start in the SDLC. Here are some of the tools:

Akto

Akto API security platform is designed specifically for modern application and product security teams. It offers wide spectrum of features which includes API discovery such as undocumented shadow APIs. Besides this, it also conducts real-time monitoring and tests for vulnerabilities like misconfigurations, broken authentication, and data exposure to improve security posture management to maintain end-to-end API security for fast paced DevSecOps workflows.

Image Source: Akto

Features:

• The platform detects internal public and third party APIs including sensitive zombie and shadow types.

• Over 100 sensitive data types including SSNs credit card information and tokens can be identified with options to add custom data types.

• Threat model API’s early to find hidden vulnerabilities

• Enables shift left testing with integration into CI/CD pipeline covering OWASP API top 10 vulnerabilities authentication authorization and business logic testing.

• AI-Powered scanning and test automation to minimize manual work.

GitHub Advanced Security with Copilot

Image Source: plain concepts

GitHub advanced security with copilot (GHAS) brings AI-powered secure coding into developers workflow. While copilot recommends code using OpenAI’s models which adds vulnerability, secret detection, scanning and dependency alerts within GitHub ecosystem.

Features:

• Copilot AI Suggestions gives secure code patterns as you type.

• Dependency graph with alerts help monitor open-source packages for known vulnerabilities.

• Secret scanning helps detect API keys and secrets before they are pushed.

• CodeQL analysis uses AI-enhanced semantic code analysis for deep scanning.

• Integrated workflows automates scanning in pull requests and CI/CD.

It is ideal for developers that use GitHub as a code platform and security teams that want integrated governance and real time scanning.

Snyk

Image source: gartner

Snyk is a developer first security platform powered by AI engine. It scans the code, containers, infrastructure-as-code files, open-source libraries to provide actionable, intelligent fixes during development phase.

Features:

• It scans for complex, context-aware vulnerabilities.

• Secure dependency management helps detect and auto-fix vulnerable packages.

• It works seamlessly with GitHub, GitLab, Bitbucket, and IDEs like VS Code.

• Secures Kubernetes, Terraform and CloudFormation files.

• Recommends remediation based on patterns learned from open source.

It is suitable for developers working in modern stacks and security teams to ensure compliance across several development teams.

DeepCode

Image source: Deepcode AI

Deepcode with AI native static analysis engine learns from large sets of codebases to help developers identify security flaws in real time in their IDE’s.

Features:

• AI-powered secure code review understands developer intent to capture issues.

• Real-time scanning in VS Code, IntelliJ, and WebStorm.

• Works with JavaScript, Python, Java, and TypeScript.

• Explains in detail as to why code is insecure, not just what’s wrong.

• Improves with time as it is exposed to real-world code.

It is ideal for developers and security teams that are looking for AI-powered code security and scalable secure code review.

Checkmarx One

Image source: source security

Checkmarx One is a cloud native application security testing platform which provides AI-powered static and dynamic analysis and codebashing. It combines SAST, SCA, container, and API security supported by machine learning to accelerate remediation.

Features:

• Focus on real vulnerabilities and reduces false positives.

• AI-based, interactive secure coding tutorials.

• Central visibility for SAST, SCA, and IaC security.

• Integrates into Jenkins, GitHub Actions, and more.

• Risk scoring based on usage and exploitability.

It is suitable for Enterprise DevSecOps teams that are looking scalable, policy-driven secure coding.

SonarQube

Image source: Sonar source

SonarQube is a well known static code analysis tool that integrates AI-powered capabilities for code security. It identifies flaws and vulnerabilities during development and CI/CD processes.

Features:

• Scans for OWASP Top 10, CWE vulnerabilities.

• Supports over 30 languages such as Java, C#, JS, Python.

• It uses Machine learning to improve accuracy in enterprise editions.

• Works with Jenkins, IntelliJ, Azure DevOps and more

• Flags areas that need developer review for potential risks.

Ideal for Developers and Security teams that are seeking feedback on code and compliance to secure coding standards.

Final Thoughts

Modern CI/CD pipelines are evolving faster, and so do the security threats. Vulnerabilities and threats like misconfigurations, broken authentication, and shadow APIs often go unnoticed. To address these security issues, Akto brings AI-powered security to the software development lifecycle by effortlessly integrating API security into pipelines. It automatically discovers all APIs, such as undocumented shadow APIs and continuously monitors and tests them for vulnerabilities like misconfigurations, broken authentication, and data exposure. Akto’s AI engine learns normal API behavior and flags anomalies in real time which allows development and security teams to act fast and address security flaws at initial stage. With contextual risk prioritization and lower false positives, teams can prioritize on what truly matters. Akto ensures your APIs are secure from code to production. Make API security an integral part of your SDLC.

Visit Akto and book a API Security demo today to get started and secure your APIs from day one.!