DEVOPSdigest

AI is undeniably ushering in a new era of innovation and efficiency for organizations across every industry. Yet, as businesses adopt sanctioned AI solutions at a breakneck pace, another revolution is quietly unfolding behind the scenes: Shadow AI.

Inclusive of all AI applications, tools and systems deployed or used without the awareness of an organization's IT team, Shadow AI is pushing boundaries in an unregulated and rapidly evolving space — which, for DevOps teams, brings a new level of both innovation and risk to software development.

The Rise of Shadow AI

Akin to Shadow IT, Shadow AI emerges when teams leverage AI-powered solutions without proper regulation or risk assessment — an action that could be as simple as using ChatGPT to generate code or plugging an AI model directly into their software. While there are a few reasons Shadow AI is on the rise, the most notable catalyst is productivity.

Since modern DevOps teams sit at the intersection of development, operations and business success, speed has become a critical factor in the software development process. Not only must organizations deliver new features and updates to stay ahead of the competition, but shorter development cycles also allow them to keep pace with evolving customer needs and technological advancements.

This increasing need for speed has pushed developers to rely on AI-powered tools, a phenomenon that closely resembles the early days of open source adoption. Despite accounting for more than 90% of software(link is external) today, open source was initially met with similar concern and scrutiny as developers flocked to it for better, faster and cheaper software development. Now, AI is following in its footsteps.

However, companies can take something away from open source's journey: trying to prevent adoption will only force developers to find a workaround. This time, organizations should embrace Shadow AI's potential and mitigate its risks rather than ban it altogether.

Two Sides of the Shadow AI Coin

Developers are drawn to AI tools because they offer transformative value, enabling improved efficiency and innovation throughout the entire software development process. Shadow AI, for one, allows developers to pull an LLM or generative pre-trained transformer (GPT) from open source repositories like Hugging Face and slot it directly into their software — much like how they pull in a logging framework or programming language — saving time while accelerating the development cycle.

With 97% of DevOps professionals already using generative AI in their workflows, organizations must also manage Shadow AI's related risks — such as concerns around data privacy, security and quality. Without proper oversight, developers may unknowingly adopt malicious AI models, which can lead to security breaches and compliance violations that negatively impact the organization's revenue and reputation. Additionally, if left ungoverned, unverified tools may generate flawed or insufficient outcomes, degrading software quality and creating functional issues.

Taking Control of Shadow AI

Ensuring Shadow AI becomes an advantage rather than a liability requires striking the right balance between unlocking its potential and mitigating its risk. Doing so includes five essential steps:

■ Establishing AI Governance and Policies: Clearly define which AI applications, tools and systems are approved and which are prohibited based on privacy, security, quality and ethical considerations.

■ Providing Employee Education: Train teams on the risks and responsibilities of AI usage, highlighting the broader impact of unsanctioned AI tools.

■ Monitoring AI Usage: Use solutions to detect unauthorized AI tools that access company data, applying proper authentication, encryption and access controls when needed.

■ Promoting Collaboration: Encourage teams to work together to foster responsible AI adoption across the organization.

■ Investing in Security: Adopt tools that monitor, detect and mitigate malicious activity to alleviate potential security threats.

AI is here to stay, which means Shadow AI will only continue to evolve. As a result, companies have to pick a side: they can either resist the shift or embrace it strategically. Those that choose the latter will be able to harness Shadow AI's full potential, transitioning it from a source of chaos and concern to an invaluable asset.