Securing The AI Frontier: Addressing Emerging Threats In AI-Powered Software Development

AI in software development is no longer a glimpse into the future – it’s here, woven into daily workflows and it’s accelerating at a breakneck pace. According to PwC’s AI Predictions report, AI has the potential to cut software development time in half, making it no surprise that companies are eager to integrate AI into their development workflows. In fact, a 2024 GitHub and Accenture study revealed that 40% of newly committed code across enterprise customers now contains AI-assisted content. And, more tellingly, 96% of developers start relying on AI-generated suggestions immediately after installing an IDE extension, indicating that AI is no longer just an experimental tool but a core component of the modern development workflow.

However, this rush toward adoption brings significant security concerns that organizations are only beginning to understand.

AI’s Role in Code Generation and Security Challenges

With over 50,000 organizations deploying millions of lines of AI-generated code daily, we’re witnessing one of the largest shifts in software development since integrated development environments became standard. While AI models excel at speed and efficiency, this often comes at the expense of security. Each AI touchpoint creates new opportunities for bad actors, who have already found creative ways to exploit these systems – from manipulating AI to suggesting compromised code to poisoning the models themselves. Combined with emerging attack vectors like data poisoning and prompt injection, this rapid adoption and inadequate oversight creates a perfect storm for potential security breaches.

The Expanding Attack Surface

As AI’s role in development expands beyond simple code completion, it’s reshaping every phase of the software lifecycle. Today’s AI tools don’t just suggest code – they generate test cases, design APIs, write documentation, and even restructure entire codebases. While AI can automate and reduce development time up to 50%, it also multiplies the potential entry points for attackers. Each new AI-assisted task represents another surface that needs to be secured, transforming what was once a relatively straightforward security measure into a complex web of interconnected vulnerabilities.

Mitigating AI Security Risks

As AI becomes embedded in software development, organizations must take a proactive approach to security. Simply relying on traditional security measures leaves blind spots that attackers are eager to exploit.

Here are five best practices to consider:

1. Establish Clear AI Security Policies: Organizations must develop comprehensive policies governing AI and large language model (LLM) usage. These policies should outline permissible AI applications, prohibited actions (e.g., AI usage on sensitive data), and security requirements for AI-generated code.
2. Apply Equal Security Standards to AI and Human-Generated Code: AI-generated code should undergo the same security reviews and testing procedures as human-written code. Automated security scanning, static and dynamic analysis, and manual code reviews should be applied consistently, regardless of how the code was created.
3. Monitor AI Integration Points: AI-driven tools interface with various stages of the SDLC, from design to deployment. Security teams must monitor these integration points to ensure AI tools are not introducing vulnerabilities. This includes scanning AI-generated code for security flaws and evaluating AI’s influence on software development decisions.
4. Adopt AI Security Frameworks: Frameworks such as the National Institute of Standards and Technology (NIST) AI Risk Management Framework provide structured approaches to mitigating AI security risks. Organizations should implement these frameworks incrementally, tailoring them to their specific environments and security requirements.
5. Enhance Developer Security Awareness: Developers must be educated on the risks associated with AI-generated code and trained to critically evaluate AI suggestions. Encouraging security-first development practices can reduce the likelihood of introducing vulnerabilities through AI-assisted coding.

The Future of AI and Cybersecurity

As AI continues to shape the future of software development, security must remain a top priority. Rather than viewing AI as a shortcut, organizations should see it as a powerful tool that enhances – but never replaces – secure coding practices. The key to success lies in staying ahead of emerging threats and embedding security into every stage of AI-assisted development. Those who take a proactive approach – adapting security frameworks, addressing AI-specific vulnerabilities, and continuously refining their defenses – will be best positioned to leverage AI’s benefits while keeping their systems and data secure.

About the Author

Matt Tesauro is a Founder and CTO at DefectDojo Inc. He is a DevSecOps and AppSec guru who specializes in creating security programs, leveraging automation to maximize team velocity and training emerging and senior security professionals. When not writing automation code in Go, Matt is pushing for DevSecOps everywhere via his involvement in open-source projects, presentations, trainings and new technology innovation.

Matt can be reached online at https://www.linkedin.com/in/matttesauro/ and at our company website https://defectdojo.com/.