Article for SiliconANGLE by Jason Bloomberg
The sudden explosion of generative artificial intelligence has created immense opportunities for companies and public sector organizations of all sizes – but with such opportunity comes increased risk.
The risks inherent in gen AI, in fact, have stopped many enterprise gen AI initiatives dead in their tracks. The knee-jerk reaction for many executives is to shut gen AI down entirely – block access to public large language models at the firewall and implement “no gen AI” policies across the board.
This overreaction to the risks of genAI is problematic for two reasons: First, it prevents organizations from building successful gen AI strategies. Second, it simply doesn’t work. Employees will find a way around such limitations, perhaps by using their phones or accessing gen AI from home – a repeat of the familiar “bring your own device” problem we now call “BYO LLM.”
The way out of this conundrum is straightforward: Implement AI governance – not to slow down innovation, but rather to remove roadblocks to adoption of gen AI in ways that are safe, legal and compliant with corporate policies.
The complex gen AI governance landscape
Given the multifaceted risks inherent in gen AI use, from bias in business decision making to exposure of sensitive information, it’s no surprise that the software vendor community smells blood in the water.
Existing governance tooling – from old-school governance, risk and compliance or GRC offerings to more modern cloud governance tools – all fall short. It’s no wonder that numerous vendors of all sizes are jumping into the gen AI governance market with a variety of offerings.
Getting a handle on this nascent market is especially challenging, because the vendors throwing their respective hats into the gen AI ring are delivering offerings that are often quite different from one another. Which approach each vendor takes depends on which risks it focuses on. To understand the gen AI governance space, therefore, it’s important to understand the risks inherent in gen AI.
Each risk category thus becomes a starting point for each vendor as it builds out a differentiated offering. Here are the most common starting points.
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