Enterprise AI security risks rise
- TechTarget and French publication IT Social both reported that enterprise use of generative artificial intelligence is widening security gaps, with prompt injection, shadow AI tools and data leakage still unresolved inside company workflows. - OWASP lists prompt injection as the top large language model risk, while NIST’s generative AI profile says organizations need monitoring, access controls and logging to manage misuse and disclosure. - The warnings land as companies move AI from pilots into daily operations, forcing security teams to govern tools that employees already use across vendors. (nist.gov)
Companies are putting generative artificial intelligence into search, coding and customer support faster than security teams can lock it down. (techtarget.com) The basic problem is simple: employees type company data into chatbots, and those systems can be manipulated by hidden instructions or careless sharing. Security teams call that prompt injection and data leakage. (owasp.org) (nist.gov) OWASP’s Top 10 for large language model applications puts prompt injection at the top of the list of risks. The category covers attacks that trick a model into ignoring its original rules and following malicious instructions instead. (owasp.org) NIST’s generative AI profile says organizations need governance, access controls, monitoring and incident response tailored to these systems. That includes tracking how models are used, who can reach them and what data they can touch. (nist.gov) TechTarget reported that security leaders are still struggling with those controls as generative AI tools spread across enterprises. The report pointed to continuing concern over prompt injection and the lack of settled defenses. (techtarget.com) French outlet IT Social described the vendor response as fragmented, with no single unified answer for protecting enterprise generative AI. Its report said companies are facing a patchwork of products and controls rather than a standard playbook. (itsocial.fr) That leaves security teams trying to govern “shadow AI” — tools employees adopt on their own without formal approval. Once that happens, sensitive documents, source code or customer records can move into systems the company does not fully audit. (techtarget.com) (nist.gov) The controls getting the most attention are practical ones: logging prompts, limiting permissions, red-teaming models and separating public chatbots from systems connected to internal data. None of those steps removes the risk, but they make misuse easier to detect and contain. (nist.gov) (owasp.org) The near-term fight is not whether companies will use generative AI. It is whether they can prove, tool by tool and prompt by prompt, that the systems are not exposing data or following the wrong instructions. (techtarget.com) (itsocial.fr)
