Traditional security approaches don’t work for AI. Generative AI technology is already transforming our world and has immense positive potential for cybersecurity and business processes, but traditional security models and controls aren’t enough to manage the security risks associated with this new technology.
We recently published a new whitepaper that examines the security challenges and opportunities from generative AI, what security must do to adapt to manage risk related to it, how a Zero Trust approach is essential to effectively secure this AI technology (and underlying data), and how different roles across your organization must work together for effective AI security.
AI presents new types of problems that require different thinking and different solutions.
At the most fundamental level, generative AI is non-deterministic computing, which means that it doesn’t provide the exact same output each time you run it. For example, asking an image generation model to “draw a picture of a kitten in a security guard uniform” repeatedly is unlikely to generate the exact same picture twice (though they will all be similar). Static security controls assume that vulnerabilities (in the broader definition) and their exploitation so they will look exactly the same each time will not be particularly effective at detecting and blocking attacks on AI. You need controls made for AI.
Generative AI is fundamentally a data analysis and data generation technology, making the security and governance of your data incredibly important to the security of your AI applications and the reliability and trustworthiness of their outputs.
You need to have an asset-centric and data-centric security approach that can handle dynamic changes to secure AI and the data it relies on. This means you need a Zero Trust approach to effectively secure AI.
Zero Trust is simply modern security without the false assumption that a network security perimeter is enough to secure assets in it (including data). This drives a mindset shift that changes how you look at security strategy, architecture, controls, and more. Zero Trust focuses security protecting business assets inside and outside the classic network perimeter across the ‘hybrid of everything’ environments (including multiplatform, multicloud, on-premises, operational technology, Internet of Things, and more).
Another complication is that AI relies on vast amounts of data to train models, making your data a prime target for cyberattackers and elevating the importance of protecting your data. Cybercriminals are also using AI now to refine attack techniques and process the data they steal from organizations. Organizations must recognize that these threats are already happening and urgently adapt their security strategies to effectively protect their data, AI applications, business assets, and people.
By applying Zero Trust principles, organizations can reduce the risk related to AI while rapidly embracing the opportunities that this technology offers.
These strategies from the whitepaper illustrate how to manage the risks associated with AI.
We have found that there is a symbiotic relationship between Zero Trust and Generative AI where:
The Zero Trust approach to security helps you keep up with continuously changing threats as well as the rapid evolution of technology that AI represents. I will wrap this blog with a quote from the new whitepaper:
“By integrating security early and embracing Zero Trust principles, organizations can take advantage of AI while mitigating risks, much like brakes on a car enable people to safely travel faster.”
To learn more about how Zero Trust can guide this approach, visit the Zero Trust Model webpage and explore additional resources at the Zero Trust Guidance Center. Check out Mark’s List for additional resources.
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Source: Microsoft Security