ChatGPT offered a hint of what might be keeping firms on the sidelines of AI. The good news: Dymium lets you dive right in.
We asked ChatGPT to give us a list of plausible reasons why a company would sit on the sidelines of AI due to fears of data exposure or leakage. Out of the long list of answers, we’ve culled just 23 (presented alphabetically) below.
Some of these concerns may feel painfully familiar. Others might give you fresh pause. However you see your AI worries reflected in this list, there’s a silver lining: Dymium protects your data so you can get the most out of the AI revolution. Request a demo to learn how.
- Difficulty redacting data at scale. Manual redaction does not scale across large datasets.
- False sense of security from “zero retention” claims. Marketing claims may not reflect real operational behavior.
- Hallucinated reuse of proprietary data. Outputs may resemble internal data, raising concerns of indirect leakage.
- HIPAA exposure. Healthcare data shared with AI tools could violate strict protections.
- Inability to audit model behavior. Companies cannot easily inspect how models process or store inputs.
- Inability to monitor real-time leakage. Companies may not detect exposure until after damage occurs.
- Inadequate incident response playbooks. Companies may not know how to respond to AI-related leaks.
- Inconsistent vendor certifications. AI features may not be included in SOC or ISO scopes.
- Insurance exclusions for AI incidents. Cyber insurance may not cover AI-related losses.
- Lack of kill-switch controls. AI tools may not allow immediate shutdown if risk emerges.
- Leakage of credentials via prompts. API keys, tokens, or passwords may be pasted into prompts accidentally.
- Leakage of proprietary algorithms. Engineers may paste core logic or algorithms into prompts, exposing intellectual property.
- Legacy systems leaking into prompts. Old systems may expose data unintentionally through AI interfaces.
- No SOC 2 coverage for AI features. Certified controls may exclude AI-specific workflows.
- Overcollection by AI tools. Systems may collect more data than necessary by default.
- Poor data hygiene. Sensitive data may be mixed with non-sensitive data.
- Risk from open-source models. Open models may lack formal security or support guarantees.
- Risk of training models on sensitive internal data. If internal data is used in prompts or fine-tuning, the company may lose control over how that data is stored or reused.
- Screenshot leakage into multimodal tools. Images may contain hidden or overlooked sensitive data.
- Shadow AI usage by employees. Employees may use unapproved tools without security oversight.
- Supply-chain attack risk. AI tools may rely on downstream services that introduce hidden vulnerabilities.
- Unintentional normalization of risky data behavior. Once AI tools are allowed, employees may gradually relax judgment about what information is appropriate to share, increasing exposure over time.
- Vendor insolvency or acquisition risk. A change in vendor ownership could alter data handling practices overnight.
That’s a lot to worry about. But again: you don’t need concerns like these to hold you back. Talk to Dymium to learn how we’re securing firms like yours from AI data exposure threats – so you can move full speed ahead on your AI initiatives.