What is Shadow AI?
Note: When we say “AI” in this post, we’ll focus on Generative AI products, such as ChatGPT, Claude, Perplexity, Gemini, Copilot, Midjourney, DALL-E, etc.
In short, Shadow AI is a new domain of risk. It is the unsanctioned (often well-intentioned) use of AI tools and products by employees or end users, without oversight, to the ultimate detriment of the organization.
What’s changed? Thanks to the popularity of (and pressure to adopt) AI tools, now anyone in the workforce can use AI to generate believable writing, images, audio, and video quickly, cheaply, and without review.
Anyone can just copy and paste AI output just about anywhere, and governance practices have not caught up.
While this possibility represents a step change in innovation, it is not without its risks. Hallucinations, factual inaccuracies, influence and bias in training data, and company data exfiltration and exposure all add up to issues that must be thoughtfully managed.
Despite these issues, anyone can just copy and paste AI output just about anywhere, and governance practices simply have not caught up. If left unaddressed, the risks could include:
Customer harm
Litigation
Reputation damage
Lost trust and credibility
Audits and fines
Remediation costs
Could anyone in your organization be using an AI product to get ahead? If the answer is yes, it’s time to start a conversation about the risks of Shadow AI.
In the session, we unpack what Shadow AI actually looks like in a modern organization, where the risk is emerging, and what can be done. You’ll get answers to questions like:
Why is there so much pressure to adopt AI?
How can teams understand their Shadow AI risk surface area?
What are the options to remediate those risks?
