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Exploring the Intersection of Security, Governance, and Generative AI
This session emphasizes the need for a balanced approach that embraces innovation while prioritizing ethical and secure AI practices.
6 Min View
Balancing the yin and yang, or benefits and risks of generative AI is crucial to optimize an approach that embraces innovation and prioritizes ethical and secure AI practices. The need to protect intellectual property generated and utilized by generative AI is not just a legal or business concern; it’s also a security imperative.
In this archived keynote session, Sidney Madison Prescott, founder & CEO of MIRROR | MIRROR and Moonshot Productions, discusses the duality of generative AI, emphasizing its benefits in automating tasks, and enhancing productivity, while also addressing concerns about data privacy and ethical implications.
This segment was part of our live virtual event titled, “Generative AI: From Bleeding Edge to Mainstream, How It's Shaping Enterprise IT.” The event was presented by InformationWeek and ITPro Today on Aug. 22, 2024.
A transcript of the video follows below. Minor edits have been made for clarity.
Sidney Madison Prescott: Now, we talk a lot about the benefits of generative AI, but it is very much a double-edged sword. We can really focus on automating mundane tasks. How do we enhance creativity? How do we enable personalized customer experiences with generative AI?
We must be vigilant about data privacy, especially given the vast amounts of sensitive data that these systems require for training. There is also the potential for job displacement, although we try to focus more on job optimization and job satisfaction as benefits of generative AI.
But there are some ethical implications of automating roles that are traditionally performed by humans. The other key that we want to point out is really the risk of biased outputs and ethical missteps. Although we focus on pursuing efficiency and looking at the competitive advantages provided by generative AI, we must also implement rigorous safeguards to manage these risks.
So, this is really that dual-edged nature of generative AI. It really necessitates a balanced approach. So, you must embrace innovation, but you must also prioritize ethical and secure AI practices. Now, we think about those risks that are associated with AI, and specifically generative AI.
Security is a paramount concern, and the risks are multifaceted. We can have data breaches if sensitive training data is improperly secured that potentially leads to privacy violations and misuse of the data. We can potentially run into the theft of AI models.
Whether someone is reverse engineering the model or other means, it does pose a significant threat to intellectual property. Unfortunately, that would allow competitors or malicious actors to either replicate or misuse proprietary systems.
There are also concerns around adversarial attacks. This is where inputs are manipulated to deceive the generative AI into making incorrect or harmful decisions, which is a very serious threat. Those attacks can undermine the reliability and integrity of the AI systems.
It can also result in a reputational risk to your firm, and that can have potentially devastating consequences. When we talk about these systems and these advanced security measures, such as encryption, access controls, and regular model verification, these are all ways that we can mitigate risk and protect our generative AI assets from both internal and external threats.
Now, let's transition to reflect on the potential security risks that are related to the use of generative AI. When we talk about intellectual property specifically, protecting intellectual property that is both generated and utilized by generative AI is not just a legal or business concern.
It's also a security imperative. There are multiple layers of defense that we must employ to protect our systems as we start on this generative AI journey. Contractual agreements are a great way to establish clear terms of use for generative AI and confidentiality.
This is going to help your firm limit unauthorized access and the distribution of sensitive data and models. Another methodology we can use is technical obfuscation. This is like data masking.
You can use code obfuscation, and this adds that extra layer of protection, which makes it more difficult for unauthorized parties to understand or even misuse your generative AI models. Another component we can use is assessing cybersecurity measures that we have in place today.
How do we need to modify and adapt those measures? It can be anything from threat monitoring to incident response plans.
But we need to adapt those existing cybersecurity measures to embrace the journey that we go on with generative AI to continue to detect and mitigate attacks in real time, without having a gap as we transition to these new technologies.
And the last thing I'll mention is regulatory compliance. We have existing protocols in place. Everyone is familiar with GDPR, but we need to make sure that as we engineer these generative AI systems, we adhere to existing data protection laws.
These are non-negotiable in terms of safeguarding your generative AI assets. The potential for legal action typically does serve as a deterrent but think of legal action as your last resort.
Our focus should really be on prevention through this robust, multifaceted protection strategy implemented in a variety of different ways across your security landscape.
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