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Your Roadmap to Secure AI: A Recap
The world is abuzz with generative AI (gen AI), and its transformative potential is undeniable. But as organizations rush to embrace this technology, they can face a slew of challenges that can make it feel like navigating a labyrinth blindfolded. A customer service department chatbot, though efficient, occasionally responds with risky or offensive statements. A bot writes code, saving time, but the code is later found to have vulnerabilities.
These are just a few examples of the hurdles organizations encounter on their gen AI journey. In this blog, we'll shed light on these challenges and offer insights to help you successfully navigate the exciting but complex world of gen AI.
Gen AI has captured the world's imagination and as its adoption across industries accelerates, organizations grapple with the realities of how to implement gen AI effectively. While a plethora of use cases exist, choosing the right ones and ensuring successful implementation can be daunting, alternately leading to analysis paralysis or rushed, uncoordinated efforts that cause inefficiencies and heightened risk. From a security perspective in particular, the latter approach often results in the inconsistent application of controls, further exacerbating the risk.
Don’t get caught up in the hype! Harnessing gen AI’s true potential requires a thoughtful, comprehensive and strategic approach. As you look to develop and mature gen AI deployment in your organization, consider the pointers and practical steps below, which we’ve gleaned from our many discussions with customers across various industry sectors and geographies.
Navigating the Risks of Consumer v. Enterprise AI
One significant challenge lies in drawing the distinction between consumer and enterprise-grade gen AI capabilities which can lead to the proliferation of shadow AI in the organization. The allure of readily available consumer gen AI tools often clashes with the stricter requirements of enterprise environments. The use of such tools need not proliferate throughout the organization to be impactful. Even the occasional casual use of consumer gen AI tools by well-meaning staff may appear to be a harmless shortcut, it can expose sensitive data and proprietary information. This shortcut can jeopardize the organization's reputation and unintentionally expose it to legal and regulatory risk.
To avoid this scenario, organizations should provide clear guidance to their staff, often through mechanisms such as an Acceptable Use Policy, to articulate the appropriate means of responsibly integrating gen AI into their day to day workflows. This disparity underscores the need for an overarching and clearly articulated governance program that sets out the relevant guardrails and is aligned to the specific needs and risk appetite of the organization.
Sharing the Burden: Who's Responsible for AI Security?
As members of Google Cloud’s Office of the CISO, we often hear from customers that one of the most pressing concerns in gen AI implementation is effective risk management and security. Risk management of gen AI can vary depending on how the organization has chosen to use AI, whether by developing its own AI applications, using third-party provided tools, or some combination of the two.
At this point in the discussion, the question frequently posed is “who is responsible for safeguarding these gen AI systems?” The answer is both customers and AI providers, though the specific responsibilities vary depending on the approach taken. Striking the right balance requires a thoughtful approach, as roles and responsibilities shift depending on the chosen implementation strategy. Careful consideration of the AI model, application, and associated risks is paramount when making these decisions.
Choosing the right AI model or application, implementing robust governance, and adhering to security best practices are primarily customer responsibilities. Conversely, ensuring the security of their platforms, offering robust security features, and providing guidance and support to customers are key responsibilities for AI providers. At Google Cloud, we refer to this as shared fate, which goes beyond the traditional shared responsibility model and emphasizes the continuous partnership between cloud providers and customers to help achieve stronger security outcomes.
Governance: A Roadmap to Success
Irrespective of the type of implementation you choose to use, robust AI governance and oversight processes are key to securing the use of gen AI. When selecting a gen AI model or application, it's crucial to consider various factors, including data privacy, security, and regulatory compliance. Effective AI governance ensures that AI systems align with organizational values and goals, minimizes the potential for bias, and protects user privacy.
Data governance is equally crucial. Since AI relies heavily on data, organizations must manage, protect, and utilize data responsibly. Data governance ensures data quality, integrity, and security, which are fundamental to the success of any AI initiative.
Effective data governance requires asking the right questions, starting with: “How can I develop clear guidelines and oversight mechanisms for AI use at my organization?” and “How do I implement technical and policy guardrails and oversight?”
Learn to Innovate SAIF-ly
We’re often asked how to stay on top of AI developments, both technological and regulatory, and how to empower teams with the knowledge, skills, and an understanding of the risks in using AI. It’s important to recognize it isn't just about technology, but about investing in people. Staying informed about AI is no longer optional - it's vital. New use cases are emerging and problems are being solved by capable users of the technology. A workforce empowered by AI knowledge translates into innovation, increased efficiency, and a stronger competitive edge.
We believe the best way to learn AI is to actually use the models in a controlled environment - experiment with them, spend time with them, apply them in your work. How else will you discover that using AI in your enterprise will be beneficial until you have a successful pilot that demonstrates its usefulness? Getting started with pilots can seem challenging because of the inherent complexity, cost, and resourcing associated with AI technology, so organizations need more than just a "try it and see" approach. The risks associated with rapid AI adoption are real and demand thoughtful — yet rapid — consideration.
As innovation moves forward, the industry needs security standards for building and deploying AI responsibly. That’s why we introduced the Secure AI Framework (SAIF), a conceptual framework to secure AI systems. Google has an imperative to build AI responsibly, and to empower others to do the same.
Build Securely
In the realm of AI systems, cloud providers serve as crucial facilitators for their growing adoption and deployment. Gen AI foundation model performance is typically enhanced when trained on larger datasets. Cloud providers empower organizations to store, process, and serve data-driven applications on a massive scale.
With cloud as a secure foundation, implementing a secure approach to AI requires traditional and AI-specific considerations for key security domains. It reinforces the need to balance traditional security safeguards with model-specific concerns, such as prompt injection, to strengthen the governance and resilience of AI systems.
Use our best practices sheet (and the detailed guidance) to build and run AI securely:
Key Takeaways to Future Proof Your AI Implementation
By prioritizing governance, security, and continuous learning, organizations can navigate the complexities of gen AI adoption and unlock its full potential, positioning themselves to reap the benefits of this transformative technology. There’s no single approach to staying on top of AI developments, which can include aligning across the organization on AI concepts and terminology, implementing an AI skills building program, conducting persona-based training, engaging with industry peers to share best practices, or any combination thereof.
- Gen AI holds immense potential, but successful implementation requires careful planning and risk management.
- Establish clear policies, build a strong governance framework, and embrace shared responsibility for security.
- Ask the right questions, prioritize data governance, and partner with your cloud provider to unlock the full power of gen AI while minimizing risks.
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