Adopt AI Innovation without Increasing Cyber Risk

Developing artificial intelligence systems has become easier than ever, and you (or your board of directors) might be eager to start putting it to use. Adopting AI can offer unprecedented opportunities to modernize your business. For example, it can:

• Uncover new business insights to reduce costs and grow revenue
• Unlock innovative visions for new products and services
• Define new markets where you gain first-mover advantage

However, AI development is moving incredibly fast, and maintaining AI systems in complex hybrid or multicloud infrastructure is difficult and expensive. If your IT and security teams, like many, are already stretched thin, the prospect of adding new and complex technology to your already towering stack is daunting. But it doesn’t have to be. Read on to learn about solutions available today that can help you build, maintain, and secure multicloud AI workloads with ease.

AI-based apps—while they may have some sophisticated capabilities under the hood—are like any other modern app driven by data, with a wide range of human-initiated and bot-driven cyber risks and hazards, including:

• Unauthorized Access: Unvetted AI application interfaces (shadow APIs) might be unknowingly visible and accessible to bad actors, posing unauthorized access to, and misuse of, the model and training data.
• Model Exploitation: Bad actors can replicate an AI model by querying it repeatedly, allowing them to create a copy that could be used competitively or maliciously. They can also use model inversion to reconstruct AI model properties or to compile new training data sets, potentially revealing sensitive information.
• Rate Limiting and Cost Overruns: Some AI models can be overused without strict rate limiting. This can lead to service degradation, brute-force attacks, or full-fledged denial of service attacks. Abuse can lead to significant costs in addition to disruption.
• New Deployment Models: Distributed architectures or edge deployments needed for successful AI workloads may be unfamiliar. Maintaining consistency and security across these varied environments is a challenge.

Addressing these risks requires setting and enforcing appropriate access levels, data protection measures, app security controls, API authorization methods, and performance-boosting configurations across the entire infrastructure.

Each stage of the development lifecycle compounds the complexities in building, connecting, and maintaining the distributed environments needed to operate a secure, high-performing AI solution. One way to significantly reduce complexity is by using containers. They require fewer resources while offering faster deployment in numerous environments.

Another key to reducing complexity is a unified development and delivery platform. This helps the many teams involved in developing AI models and apps work collaboratively and efficiently. It also simplifies the elaborate process of training the AI models and deploying AI data and applications across your multifaceted infrastructure.

A unified platform can help your teams overcome the development and management challenges with simple configurations and deployment in the cloud or on premises. However, that platform also needs to build in security at every step of the process.

APIs can be a major risk for AI-based apps, as these connections are key for operation. Adding API security to the AI model and app deployments not only keeps them secure and available but also adds additional governance.

Distributed apps or ones running at the edge require secure connectivity for seamless operation across clouds or customer locations while protecting apps and data against threats or unauthorized access. Apps and AI models also need protection from security threats ranging from bots to vulnerabilities. Deploying an additional layer of protection designed for web apps prevents attacks that can cause slowdowns or a data breach.