A new survey by Gartner suggests that 34% of organisations are either already using or implementing artificial intelligence (AI) application security tools to mitigate the accompanying risks of generative AI (GenAI).
The Gartner Peer Community survey was conducted from April 1 to April 7 among 150 IT and information security leaders at organisations where GenAI or foundational models are in use, in plans for use, or being explored.
The survey revealed that 26% are currently implementing or using privacy-enhancing technologies (PETs), ModelOps (25%) or model monitoring (24%) (see Figure 1).
Figure 1. Organisations using or planning to use tools to address risks related to generative AI (Percentage of Respondents)
“IT and security and risk management leaders must, in addition to implementing security tools, consider supporting an enterprise-wide strategy for AI TRiSM (trust, risk and security management),” said Avivah Litan, distinguished VP analyst at Gartner.
“AI TRiSM manages data and process flows between users and companies who host generative AI foundation models and must be a continuous effort, not a one-off exercise to continuously protect an organisation.”Avivah Litan
IT is ultimately responsible for GenAI security
While 93% of IT and security leaders surveyed said they are at least somewhat involved in their organisation’s GenAI security and risk management efforts, only 24% said they own this responsibility.
Among the respondents who do not own the responsibility for GenAI security and/or risk management, 44% reported that the ultimate responsibility for GenAI security rested with IT. For 20% of respondents, their organisation’s governance, risk, and compliance departments owned the responsibility.
The risks associated with GenAI are significant, continuous and will constantly evolve. Survey respondents indicated that undesirable outputs and insecure code are among their top-of-mind risks when using GenAI:
- 57% of respondents are concerned about leaked secrets in AI-generated code.
- 58% of respondents are concerned about incorrect or biased outputs.
“Organisations that don’t manage AI risk will witness their models not performing as intended and, in the worst case, can cause human or property damage,” said Litan. “This will result in security failures, financial and reputational loss, and harm to individuals from incorrect, manipulated, unethical or biased outcomes. AI malperformance can also cause organisations to make poor business decisions.”