CTO James White’s Take on the AI Security Landscape
The promises of AI for enterprises are endless, and industries of all types are throwing their hat in the ring—from healthcare to financial institutions to government and more. One thing is clear: this appetite isn’t slowing down anytime soon. Securing AI is critical in its adoption and right now, its taking the shape of a pyramid.
The Apex of the AI Pyramid
At the very top of the AI ecosystem sits the rarified air of foundation model training. This is a domain occupied by a select few, wielding immense computational resources and navigating the complexities of massive datasets. The financial investment required is staggering, raising legitimate questions about its long-term viability, even for leading players. Security within this phase is paramount, encompassing every stage from data acquisition and preprocessing to model training, testing, and deployment. Protecting these crown jewels of the AI world is not just a technical necessity, but a business imperative. This is something these companies are uniquely positioned to do as they are across all of these facets throughout the training process.
The Middle: Secure the Inference, Secure the Masses
Beyond the apex, a vast landscape of businesses and use cases are emerging, ready to leverage the power of pre-trained foundation models for inference. This is where the true democratization of AI occurs. For these organizations, security focuses on protecting their own infrastructure, data, and ultimately, their users. Treating foundation models as opaque black boxes is the most prudent security posture. This approach minimizes the attack surface and allows for robust security measures to be implemented around the model and application, regardless of the model’s internal complexities.
The Base: A Mile Wide But an Inch Deep
The generative AI boom has witnessed a flurry of companies attempting to pivot from securing traditional machine learning model training to securing the entire generative AI lifecycle, including inference, which I’ll get to soon. This broad approach, while well-intentioned, often results in solutions that are a mile wide but an inch deep. Trying to secure such a vast and complex space dilutes focus and ultimately fails to address the specific nuances of each stage, particularly the critical inference layer.
Holistic Security for AI Inference
The rapidly evolving nature of AI innovation is shaping a reality in which security solutions must be as adaptable and advanced as the threats they face. The only way to secure AI in this way is at the inference layer, which requires a holistic approach that encompasses three core areas:
- Defense: Robust defense mechanisms are crucial. This includes input validation and sanitization to prevent prompt injection attacks, output filtering to mitigate harmful content generation, and runtime monitoring to detect anomalous behavior. Understanding the specific use case is paramount, as different applications will have unique security requirements. Furthermore, considering cross-cutting attacks that target the entire AI pipeline is essential.
- Offense: Effective red teaming is vital for identifying vulnerabilities in opaque foundation models and the applications built upon them. This involves adversarial testing, exploring potential attack vectors, and developing mitigation strategies. This proactive approach helps organizations stay ahead of emerging threats and ensure the robustness of their AI deployments.
- Governance, Regulation, and Compliance (GRC): Navigating the evolving regulatory landscape is a critical aspect of AI security. Each jurisdiction and industry vertical will have its own set of rules and regulations. While adhering to the “paper defense” is necessary, it’s equally important to implement practical security controls that translate these requirements into tangible protection.
Securing AI Inference in Your Organization
To effectively secure the future of AI inference, organizations must take the following concrete steps:
- Establish an AI Review Group: This cross-functional group should include representatives from technology, legal, and security teams to ensure a comprehensive approach to AI governance and risk management.
- Implement an AI Application Proposal Process: This process should require teams to clearly define the intended use case, data sources, and target regions for each AI application. This allows for a thorough risk assessment and the implementation of appropriate security measures.
- Define Security Minimum Requirements: Establish clear security standards that application development teams must adhere to and application security teams must verify. This provides a baseline level of security across all AI deployments.
Here’s a key resource to help you get started.
The future of AI hinges on our ability to secure it. By focusing on a holistic approach to inference layer security, businesses can unlock the transformative potential of AI while mitigating the risks.
Ready to learn more about how you can implement holistic AI security controls in your organization? Schedule a demo now here.
