Five key security challenges for artificial intelligence

According to recent estimates, the global artificial intelligence market will exceed 184 billion dollars by 2024 at a compound annual growth rate of 35%. By all accounts, the pace and scope of AI innovation continues to grow. But new data shows that the evolution of AI poses serious challenges to cloud service security. The incredible pace of AI innovation, combined with the relative newness of the technology and an overemphasis on speed of deployment, creates complex challenges that organizations must understand and address. In this article, we look at the top five AI security challenges for cloud services, their importance, and how to address them.

1. Speed of innovation

The first challenge is the pace of development. AI continues to accelerate and AI innovation is leading to features that promote usability at the expense of security issues. Controlling these changes is difficult and requires constant research, development and implementation of advanced security protocols. AI services from cloud providers continue to expand and evolve, with new AI models, suites, and resources constantly emerging. For example, this year alone, cloud providers have introduced a number of third-party AI models: Google Vertex added Llama 3, Anthropic, and Mistal, Azure OpenAI announced the addition of Whisper, GPT-4o, and GPT-4o mini-, and Amazon Bedrock introduced Cohere, Mistral AI, Stability AI, and other new Command R models. These rapid and continuous changes create challenges for security teams. For example, security settings for new AI services must be properly configured, and visibility into AI assets and risks must be maintained.

2. AI shadow

The next problem is found when it comes to visibility, security teams often lack it, especially when it comes to AI activities. Shadow AI, i.e. unknown and unapproved AI technologies used within an organization, often results in security teams failing to fully understand the risks of AI in the cloud. Shadow AI can hinder the adoption of security best practices and policies, resulting in an increased attack surface and higher risk to the organization. Separation of security and development teams is one of the key drivers of shadow AI. Other challenges include the lack of AI security solutions that provide security teams with full visibility into all AI models, kits and other resources deployed in an environment, including shadow AI.

3. Early-stage technologies

The third challenge is AI security, which is still in its early stages, so there is a lack of extensive resources and experienced experts. To secure AI services, organizations often have to develop their own solutions without external guidance or paradigms. While service providers offer best practices and restrictive configurations for AI services, security teams may lack the time, capabilities, or awareness to implement them effectively.

4. Non-compliance with regulatory requirements

The fourth challenge is meeting new regulatory requirements. Which require a delicate balance between fostering innovation, ensuring security, and complying with new legal regulations. Companies and policies need to be flexible and responsive to new regulations around AI technologies. Much of this policy is still emerging, but there are already signs of adoption of the EU AI regulation.

The changing regulatory landscape presents a significant challenge for organizations already struggling to comply with multi-cloud technologies, so it is important to ensure full transparency of AI models, resources and usage, which is difficult to achieve with shadow AI. AI resources, like cloud assets, are launched and terminated at a certain scale and frequency, so they need to be automated throughout the compliance lifecycle, which includes the following items:

• Inventorying AI assets, risks and activities;
• Matching assets and risks to compliance frameworks and controls;
• Continually monitoring and updating the status of compliance controls;
• Addressing non-compliance issues;
• Communicating progress to stakeholders on a regular basis.

5. Resource Management

The fifth problem is caused by the fact that when new services are introduced, resources are often mismanaged. Users often neglect to properly configure settings related to roles, departments, users, and other assets, which can pose a significant risk to the environment. As mentioned above, many organizations fail to properly configure security settings for AI services across domains. This jeopardizes the security of published AI models, access keys, superuser resources, and published assets.

Solving the AI security problem

Fortunately, organizations can protect AI innovation at scale with a new solution called AI Security Practice Management (AI-SPM). It's a cloud-based security solution that provides a number of security features such as shadow AI, advanced risk detection and deployment prioritization, and automated compliance features that give security teams complete visibility into the status of AI deployments. These features are based on native integration with the Cloud Application Protection Platform (CNAPP) and CNAPP's ability to provide comprehensive coverage and risk detection across the entire cloud.