Building a Solid Framework for Generative AI Security
Published Date: 15/06/2024
The proliferation of generative artificial intelligence (gen AI) email assistants such as OpenAI's GPT-3 and Google's Smart Compose has raised concerns about AI security.
How many companies intentionally refuse to use AI to get their work done faster and more efficiently? Probably none the advantages of AI are too great to deny. However, generative AI also comes with risk. According to the IBM Institute for Business Value, 96% of executives say adopting generative AI makes a security breach likely in their organization within the next three years.
CISA Director Jen Easterly said, “We don’t have a cyber problem, we have a technology and culture problem. Because at the end of the day, we have allowed speed to market and features to really put safety and security in the backseat.” And no place in technology reveals the obsession with speed to market more than generative AI.
AI training sets ingest massive amounts of valuable and sensitive data, which makes AI models a juicy attack target. Organizations cannot afford to bring unsecured AI into their environments, but they can’t do without the technology either.
To bridge the gap between the need for AI and its inherent risks, it’s imperative to establish a solid framework to direct AI security and model use. To help meet this need, IBM recently announced its Framework for Securing Generative AI. Let’s see how a well-developed framework can help you establish solid AI cybersecurity
Securing the AI pipeline involves five areas of action
1. Securing the data How data is collected and handled
2. Securing the model AI model development and training
3. Securing the usage AI model inference and live use
4. Securing AI model infrastructure
5. Establishing sound AI governance
Now, let’s see how each area is oriented to address AI security threats.
IBM is an industry leader in AI governance, as shown by its presentation of the IBM Framework for Securing Generative AI. As entities continue to give AI more business process and decision-making responsibility, AI model behavior must be kept in check, monitoring for fairness, bias and drift over time. Whether induced or not, a model that diverges from what it was originally designed to do can introduce significant risk.
IBM is a multinational technology and consulting corporation that provides hardware, software, and services to clients across various industries. The company is a leading provider of AI solutions, including the IBM Framework for Securing Generative AI.
FAQs:
Q: What is the risk of adopting generative AI?
A: According to the IBM Institute for Business Value, 96% of executives say adopting generative AI makes a security breach likely in their organization within the next three years.
Q: What are the five areas of action for securing the AI pipeline?
A: The five areas of action are securing the data, securing the model, securing the usage, securing AI model infrastructure, and establishing sound AI governance.
Q: What is AI governance?
A: Artificial intelligence governance entails the guardrails that ensure AI tools and systems are and remain safe and ethical.
Q: What is the IBM Framework for Securing Generative AI?
A: The IBM Framework for Securing Generative AI is a well-developed framework that helps organizations establish solid AI cybersecurity by addressing AI security threats.
Q: Why is it important to secure AI models?
A: AI models are a juicy attack target, and without proper AI model security, the downside risk can be significant, leading to reputational damage and legal headaches.