Gartner Predicts 40% of Firms Will Face AI-Driven Security Incidents by 2030

By 2030, Gartner predicts 40% of global organisations will face security and compliance incidents due to unauthorised AI tools (shadow AI). Gartner advises CIOs to establish clear policies, conduct regular audits, and incorporate GenAI risk evaluation into SaaS assessments. Additionally, they warn of rising technical debt and ecosystem lock-in from GenAI, recommending open standards and modular AI architectures.

Gartner has predicted that by 2030, more than 40% of global organisations will face security and compliance incidents due to the use of unauthorised AI tools, commonly referred to as "shadow AI." 

The prediction stems from a recent survey of cybersecurity leaders, which revealed that 69% of respondents have evidence or suspect that employees are using public generative AI (GenAI) at work.

Commenting on this, Mayur Upadhyaya, CEO at APIContext, said: 

"Shadow agentic AI is the next frontier of shadow IT. We’re no longer just talking about unsanctioned apps or BYOD. We’re now facing autonomous tools that can take actions, connect to internal systems, and trigger workflows without visibility or control. Gartner’s prediction that over 40% of global organizations will suffer incidents from unauthorized AI tools by 2030 isn’t just plausible, it’s conservative if proactive measures aren’t taken.

The real risk is not just data leakage, it’s the creation of unmonitored, persistent access points. Agentic tools using APIs to “self-serve” critical functions can easily connect to undocumented MCP endpoints, leaving no audit trail and bypassing existing security controls. Most enterprises don’t yet have a strategy for managing this class of interaction and that’s where the danger lies.

Without guardrails for AI identity, scope, and delegation, these tools can quickly create systemic risk. Just as we learned to monitor user access and API usage, we now need the same discipline for autonomous agents. This isn’t just about blocking tools, it’s about making trusted access observable and enforceable".

Risks posed by shadow AI

The use of GenAI tools presents several risks for organisations, including intellectual property (IP) loss, data exposure, and compliance issues. Gartner highlights that these risks are not new. For example, in 2023, Samsung had to ban the use of GenAI internally after employees shared sensitive source code and meeting notes with ChatGPT.

In response to these risks, Gartner suggests that Chief Information Officers (CIOs) should take proactive steps to mitigate potential threats. “To address these risks, CIOs should define clear enterprise-wide policies for AI tool usage, conduct regular audits for shadow AI activity, and incorporate GenAI risk evaluation into their SaaS assessment processes,” said Arun Chandrasekaran, distinguished VP analyst at Gartner.

Wider industry concerns on shadow AI

Gartner’s findings align with similar studies. Last year, Strategy Insights reported that over a third of organisations in the US, UK, Germany, the Nordics, and Benelux had challenges monitoring unauthorised AI use. In the same year, RiverSafe noted that 20% of UK firms had exposed potentially sensitive corporate data due to employee use of GenAI. Additionally, a survey from 1Password revealed that 27% of employees have worked with non-sanctioned AI tools.

Technical debt and GenAI use

Beyond shadow AI, Gartner also highlighted the growing concern of technical debt associated with the use of GenAI. By 2030, Gartner predicts that 50% of enterprises will face delays in AI upgrades and increasing maintenance costs due to unmanaged technical debt. This issue arises when the long-term costs of maintaining, fixing, or replacing AI-generated content such as code, content, and designs outweigh the initial benefits.

“Enterprises are excited about GenAI’s speed of delivery. However, the punitively high cost of maintaining, fixing or replacing AI-generated artifacts can erode GenAI’s promised return on investments,” said Chandrasekaran.

To prevent technical debt from becoming a long-term issue, Gartner advises that enterprises establish clear standards for reviewing and documenting AI-generated assets and track technical debt metrics in IT dashboards. By doing so, organisations can proactively manage and reduce the risk of costly disruptions.

The risks of ecosystem lock-in and erosion of skills

Another potential downside of GenAI usage is the risk of ecosystem lock-in, where companies become overly dependent on a single vendor. Gartner also warned about the erosion of skills within organisations due to over-reliance on GenAI tools.

“To prevent the gradual loss of enterprise memory and capability, organisations should identify where human judgment and craftsmanship are essential, designing AI solutions to complement, not replace, these skills,” said Chandrasekaran.

CIO strategies for managing AI adoption

In order to manage these challenges, Gartner recommends that CIOs focus on prioritising open standards, open APIs, and modular architectures when designing their AI stack. This approach can help avoid over-dependence on a single vendor, providing organisations with more flexibility in the long term.

As businesses continue to embrace the potential of GenAI tools, it is clear that while these technologies offer significant advantages, they must be carefully managed to mitigate associated risks. The onus is on organisations to adopt clear policies, review AI-generated content regularly, and be mindful of the long-term implications of unchecked AI use.