What is AI-assisted software development?
AI-assisted software development integrates machine learning and AI-powered tools into your coding workflow to help you build, test, and deploy software without wasting resources.
Instead of manually handling every aspect of coding, you now have AI-driven tools to automate repetitive tasks, deliver better accuracy, and get software into customers’ hands faster. These tools range from code completion and bug detection to automated testing and DevOps tweaks, making the entire software development lifecycle (SDLC) more accessible and productive.
But AI isn’t here to replace you: It’s here to lend a hand. With intelligent recommendations and automation of routine tasks, AI lets you focus on creativity, strategy, and problem-solving instead of repetitive coding. All in all, productivity gets a major boost, with devs completing 26% more tasks on average.
Plus, with projections indicating that 3 out of 4 enterprise software engineers will be using AI code assistants by 2028, these tools are more than just a trend—they’re the future of software development.
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The role of AI in software development
Back in 2017, Andrej Karpathy famously hinted at Software 2.0—a future where AI not only assists but also revolutionizes software creation. Today, AI is an everyday tool in the developer’s toolkit, already reshaping how software is built, tested, and deployed.
But while AI delivers remarkable enhancements, it can be a major challenge to integrate it into your workflow. Luckily, with the support of a dedicated AI risk management solution, you can overcome the shortcomings and unlock all the pros of AI-assisted code.
First, let's break down both the benefits and drawbacks of embracing AI in software development.
Benefits of AI in software development
AI is streamlining the developer experience by making it more intuitive and consuming fewer resources through three core benefits:
Enhanced productivity: AI automates routine tasks like generating code, completing snippets, and maintaining documentation, meaning you can focus on problem-solving.
Improved accuracy of solutions: Advanced code scanning and error detection make AI great at spotting bugs and vulnerabilities, resulting in higher overall code quality.
Increased accessibility: AI-powered tools simplify coding workflows and assist non-specialists, though foundational programming knowledge is still necessary for effective use.
Challenges and risks of AI in software development
While AI offers many advantages, its strengths are most apparent in simpler tasks like repetitive coding, drafting initial code versions, and small updates. Avoiding the following pitfalls—no matter the project size—still demands human expertise:
Quality and reliability issues: AI can sometimes produce inconsistent or overly verbose code; outputs can also lack transparency while harboring hidden biases.
Limited support for complex cases: AI excels at routine tasks but struggles with niche programming languages, project-specific contexts, and interoperability with existing architectures.
Security concerns: Issues like licensing conflicts from public code repositories, code injections, and outdated security practices make AI security risks a top priority.
Overreliance on AI: There’s a danger that leaning too heavily on AI tools may erode your own problem-solving skills as a developer.
How AI is transforming the software development lifecycle
The impact of AI has been far-reaching, enabling intelligence at every stage of software development. Here’s a breakdown of how companies have used AI for each step in their SDLC:
1. Planning: AI converts high-level ideas into step-by-step plans by organizing brainstorming sessions, setting clear roadmaps, and pinpointing key objectives.
Example: AI can help you start a new app project by setting deadlines and allocating resources based on successful past projects.
2. Analysis: Using historical data and market trends, AI can define project scope, prioritize features, and identify potential risks early on—meaning your project starts on solid ground.
Example: AI can review data from previous releases to suggest which features will deliver the most value and where potential risks might lie.
3. Design: AI helps craft detailed system architectures and design prototypes. By evaluating various design patterns and simulating potential outcomes, it sets you up to deliver the best code security and performance.
Example: AI can generate multiple design blueprints for your system and recommend the one that best balances your performance and security needs.
4. Coding: Acting as your coding co-pilot, AI generates code snippets, provides smart auto-completion, and integrates real-time code scanning. This slashes time wasted on boilerplate code and streamlines your development process.
Example: As you type a function header, AI can suggest all the code needed, significantly reducing the time you would normally spend writing boilerplate code.
5. Testing: AI revolutionizes testing by automatically generating and executing test cases. It simulates multiple scenarios to catch edge-case bugs and vulnerabilities, ensuring each line of code is thoroughly vetted.
Example: AI can create and run hundreds of test scenarios for your API endpoints overnight, prioritizing issues for you to address by morning.
6. Deployment: AI perfects CI/CD pipelines by analyzing historical deployment data to predict failures, recommend adjustments, and ensure minimal downtime during rollout. This gives you smoother, more predictable deployments.
Example: AI can discover that a particular microservice often fails during high traffic by reviewing previous deployment logs; it will then advise you to schedule the update during off-peak hours.
7. Maintenance: After deployment, AI continuously monitors system performance, error logs, and user feedback, identifying areas for refactoring or updates. This turns maintenance into an ongoing process that keeps your software sharp.
Example: AI can detect a higher API response time after a service update, uncover the underlying issue, and send an alert with a new suggested patch.
The arrival and advancement of AI means SDLCs are bound to experience a shift from a traditional design approach to platform-oriented thinking where a single, integrated system supports multiple stages of the software development lifecycle.
Looking ahead, trends like autonomous SDLCs, AI-augmented threat modeling, and AI-powered continuous testing, promise a leaner, more agile, and secure development process that keeps you ahead of the competition.
Top 4 use cases for AI-assisted software development
Let’s take a closer look at the most impactful ways AI is currently being used—not just as a tool, but as a strategic partner in rethinking your entire SDLC.
AI-powered code generation
AI coding assistants offer intelligent code suggestions and auto-completion directly within your IDE with full context over your code set. If access to your code repository is not feasible, you can use prompt engineering with any AI coding assistant to generate tailor-made code snippets—this is the essence of code synthesis. It’s like having a brainstorming partner who always has a suggestion ready, making your coding process faster and more intuitive.
Automated testing and error diagnosis
AI now handles code reviews and bug detection automatically, unlocking the ability to anticipate potential issues before they become problems. It generates and prioritizes test cases so that critical tests get the attention they need, reducing the time and resources spent on manual testing. This means fewer bugs in production and a more solid final product.
Security and vulnerability detection
Gone are the days when security checks were manual and time-consuming. AI tools assist in code scanning to detect common security vulnerabilities, though human oversight and manual reviews remain essential. With proactive threat identification, these tools help you patch issues before they turn into major security breaches—a big win with the growing number of code security threats companies face nowadays.
Improved DevOps
AI analyzes code changes, test results, and production metrics—giving you the data you need to boost performance and quality. It also estimates the time required for various tasks, letting you plan ahead and allocate resources properly. All of this results in streamlined DevOps processes that run with minimal downtime.
Top 5 must-know AI-powered tools for software development
Whether you’re brainstorming ideas or deep in the debugging trenches, AI-powered tools are appearing to support every phase of the SDLC. These solutions let you focus on your core function—creating secure, high-quality software.
There are numerous options available, but these five AI coding tools are must-haves, no matter your exact industry:
Aider: A versatile tool that streamlines code generation and offers intelligent suggestions to speed up your coding process.
GitHub Copilot: Perhaps the poster child for AI-assisted coding, Copilot offers auto-completion and context-aware code suggestions that feel like magic.
Cursor: Known for its intuitive interface, Cursor assists in writing clean code and refactoring existing projects with zero disruptions.
Windsurf: Ideal for managing large-scale projects, Windsurf integrates with various development platforms to optimize your workflow.
Tabnine: This tool uses deep learning to offer smart code completions that help you avoid common pitfalls and write more secure code.
While these tools all have unique characteristics, there is no one-size-fits-all solution. Trying out more than one will let you see which fits best into your workflow. Look for a tool that jump-starts productivity but also assimilates smoothly with your existing ecosystem.
How can you adapt your software security framework for AI?
As AI becomes an integral part of software development, companies must adapt their security framework to successfully mitigate AI security risks. Industry-standard frameworks don’t yet specialize in AI security, but they still provide a solid foundation.
Frameworks like NIST’s Secure Software Development Framework (SSDF), OWASP Software Assurance Maturity Model (SAMM), and the Building Security in Maturity Model (BSIMM) offer best practices that can be tailored to AI-powered development.
Keep in mind that identifying where these frameworks overlap and where they need AI-specific extensions is useful for a hybrid security strategy that protects your entire SDLC from planning to deployment; Codific offers a great comparison resource for this.
When crafting your security framework for AI-powered SDLC, it’s also best to follow some key recommendations and discover the best solutions to safeguard your product.
Best practices to secure your AI-powered SDLC
To reinforce code security in your AI-driven workflow, we offer the following advice:
Assess AI tools before integrating them: Get familiar with AI-specific security risks, and conduct a comprehensive threat assessment to understand potential vulnerabilities.
Prioritize security when adopting AI solutions: Ensure that AI-driven tools interact securely with your existing systems, minimizing exposure to third-party and supply chain risks.
Enforce AI-specific CI/CD testing: Add tailored security tests for AI-generated code, focusing on format validation, bias detection, and manipulation risks.
Encourage cross-disciplinary collaboration: AI security isn’t only for developers to worry about—engage security teams, data scientists, and compliance experts.
Stay up to date: Make sure you’re on top of AI SecOps best practices at all times.
How does Wiz support AI and code security?
At Wiz, the mission is clear: Empower developers to build faster in the cloud, without compromising security.
Wiz takes a dual approach to AI security:
AI-powered security for the cloud: Wiz AskAI acts as an assistant, helping teams streamline security workflows and identify vulnerabilities. AskAI transforms cloud security operations by making them more intuitive and accessible. Its AI remediation capability generates straightforward steps to address vulnerabilities, while the AI investigation feature provides in-depth analysis of security issues that would typically require specialized expertise. For teams needing custom security policies, AskAI can generate Rego code without requiring deep policy-as-code knowledge.
Security for your AI Applications: Wiz AI-SPM (AI Security Posture Management) ensures your AI models and data pipelines are protected against emerging threats, meaning your entire ecosystem will remain resilient at scale.
With end-to-end vulnerability management, infrastructure-as-code (IaC) scanning, and compliance automation for NIST and other frameworks, Wiz provides comprehensive security coverage.
What’s next?
The future of software development is evolving rapidly. The AI trend means developers worldwide are no longer just traditional coders but orchestrators of AI-driven ecosystems.
As you embrace these advancements, you’ll notice that every phase of the SDLC becomes more streamlined, allowing for secure, faster product development. With tools that enhance code security and frameworks that adapt to modern threats, now’s the perfect time to integrate these AI-powered solutions into your workflow—a journey Wiz can help you with.
Ready to learn more? Discover the latest from Wiz CloudSec Academy, or see Wiz in action with a live demo.
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