3 Big Generative AI Risks and How to Manage Them

The 3 Biggest Risks from Generative AI - And How to Deal with Them

Generative AI is exploding in popularity. More businesses want to explore it to boost productivity and create content. However, most enterprises are still only at the exploratory stage Less than 5% use generative AI in production, according to Gartner. Big, transformative projects are rare. Firms are starting small, through pilots at the edges before bringing services into the core.

But professionals are already using generative AI, with or without permission. Per O'Reilly, 44% of IT professionals use AI for programming and 34% experiment with it. As adoption grows, what are the biggest risks with generative AI that businesses must manage?

Generative AI Risk 1: Privacy and Data Protection

With enterprise generative AI, companies often send data to vendor's cloud environments. This isn't new. But CIOs worry how vendors store and use the data, like for training models.
You must trust vendors have good security. But mistakes happen in cloud systems. If confidential data leaks, vendors won't be liable - you will.

Generative AI Risk 2: Unacceptable Inputs and Inaccurate Outputs

Employees could compromise decision-making by misusing confidential data. Outputs could contain harmful inaccuracies and biases.
Executives must ensure people use data and AI acceptably, not compromising the business. Outputs must be vetted for inaccuracies.

Generative AI Risk 3: New Cybersecurity Threats

Hackers can attack models, swap out data, and more. Old security tools like endpoint protection won't help.
You'll need new security focused on protecting data models themselves. It's a completely new threat vector.

How to Manage the Risks

- Define acceptable use policies
- Classify data
- Approve user requests
- Review processes regularly

Take things step-by-step. Solutions are evolving rapidly alongside risks. With preparation, businesses can harness generative AI safely.