Managing the Double-Edged Sword of AI-Generated Code

Cybersecurity remains a top priority for organizations in 2026, with supply chain exposures continuing to pose significant challenges. That risk increases with the adoption of AI by both vendors in the supply chain and attackers looking to exploit vulnerabilities. As organizations assess the evolving cyber risk landscape, anticipate emerging threats and plan strategically for the year ahead, a deeper analysis should be made on the risks posed by the increased reliance on AI by partners at all levels of the supply chain.

Understanding the risks

The first step in managing cyber risks associated with AI adoption is understanding the vulnerabilities and risk exposures. These primarily surface in two areas:

1.       AI-generated code introduces systemic vulnerabilities into the supply chain

AI is rapidly becoming an integral part of software development, assisting with everything from writing full functions to fixing bugs. These tools promise faster delivery and lower costs—but that speed comes with trade-offs. Many AI-generated outputs lack secure coding practices, and an overreliance on them is eroding developers’ ability to critically evaluate code before deployment.

The consequence is often poorly vetted machine-written code entering products and services unchecked. These weaknesses do not stay isolated; they cascade through the supply chain. Vendors may unknowingly embed insecure codes into critical systems used by banks, hospitals and government agencies.

2.       AI is aiding attackers

Software developers are not the only ones leveraging AI: attackers are as well. Organizations are rapidly adopting AI-powered tools to detect threats faster, identify anomalies in massive datasets and automate responses. However, the very same technology is being weaponized by attackers, giving rise to a new wave of cyber threats that are faster, smarter and increasingly difficult to defend against.

While human developers may need weeks or even months to uncover a vulnerability in code, malicious AI systems can scan vast codebases in mere hours—identifying exploitable flaws and weaponizing them at a pace humans cannot match. As a result, organizations are not only introducing insecure code into their environments but doing so in a landscape where adversaries are more capable than ever of exploiting those weaknesses.

Managing the risks

Organizations need to rethink their strategy for software assurance, adopting a proactive approach to managing vendor risks. No longer simply a compliance exercise, vendor due diligence cannot stop at the first tier of suppliers — it must extend into the “fourth-party” risks buried deep in the software bill of materials (SBOM).

Organizations should be taking the following safeguards with their vendors to protect against an AI-driven flood of insecure software in their supply chain:

• Employ secure-by-design practices: Through embedding security practices into every stage of the software development lifecycle rather than treating it as an afterthought, organizations are better able to minimize vulnerabilities before deployment, thus ensuring software is safe right out of the gate.

• Continuously review code: Continuously reviewing code aids in the early detection of AI-introduced vulnerabilities. Unchecked vulnerabilities can cascade through vendors and downstream partners, embedding into critical infrastructure. Ongoing code review reduces the chance that insecure code propagates across the supply chain, potentially entering critical systems. It also catches vulnerabilities before attackers can exploit them.

• Conduct real-time vulnerability scanning: Automated real-time scanning detects vulnerabilities that traditional testing might miss, reducing the chance of insecure code entering production environments. In turn, this reduces the exposure window and protects against cascading supply chain risks. It also provides ongoing assurance and audit trials, helping organizations meet regulatory requirements and maintain trust with customers and partners.

The bottom line is that the risk is clear: unchecked AI-generated code would not just break products— it could compromise entire ecosystems. 

At a time when AI accelerates both innovation and risk, cybersecurity can no longer be a reactive approach. AI should be treated as both an ally and a risk, with equal priority placed on investing in AI-driven efficiencies and defenses, as well as planning for defense against AI-driven attacks. Strong governance, regular testing and employee education around emerging threats are imperative. 

Organizations must embed security into every stage of development, extend vendor oversight deep into the supply chain and adopt continuous safeguards like code reviews and real-time vulnerability scanning. These measures are not just best practices—they are essential for preserving trust, resilience and the integrity of entire networks.