Shadow AI

With the rapid advancements in artificial intelligence and the proliferation of accessible AI tools, enterprises are experiencing a new challenge: Shadow AI. This phenomenon refers to the deployment or use of AI tools and models within an organization without the formal oversight, approval, or knowledge of IT and security teams. Shadow AI poses significant risks, from data security to compliance violations, yet it is often overlooked by companies eager to harness AI capabilities. In this post, we’ll delve into what Shadow AI is, how security teams can detect it, and how to build an effective solution to manage and mitigate its risks.

What is Shadow AI?

Shadow AI includes any unauthorized or unsanctioned use of AI models, tools, or applications in an organization. Similar to Shadow IT, Shadow AI may arise when departments or individuals adopt AI tools or services to solve business problems or streamline processes without consulting the IT or security team. With the increased accessibility of AI tools and cloud services, users can deploy complex machine learning (ML) models with little technical knowledge, leading to unintended security and compliance risks.

Practical Examples of Shadow AI:

1. Marketing Teams Using Unapproved AI Models for Analysis
   1. A marketing team decides to analyze customer sentiment using a free, publicly available NLP model. Without understanding the model’s security implications or the quality of training data, they expose sensitive customer data during analysis.
2. Research Departments Leveraging Public AI APIs
   1. A research team uploads proprietary data to a third-party AI tool for quick analysis without understanding the data handling practices of that tool provider. This may lead to data leakage, violating privacy or compliance requirements.
3. Developers Experimenting with Unapproved OpenAI APIs
   1. Developers may use OpenAI APIs or similar services to generate code snippets, perform data transformations, or test AI-enhanced features in applications. However, without IT approval, this can lead to exposure of internal code and operational data, especially if the API is not designed for enterprise-grade security.

How Can Security Teams and CISOs Detect Shadow AI in Their AWS Cloud?

Detecting Shadow AI is challenging because it often operates outside of traditional IT or security-approved channels. Many AI tools run on cloud platforms, like AWS, which can compound the challenge by dispersing resources across various teams or projects. Here are some strategies and tools to help detect Shadow AI:

Step 1: Inventory AI Resources in AWS

AWS provides several services for building, deploying, and managing AI models. Security teams should start by creating an inventory of resources across AI/ML services, including:

• Amazon SageMaker instances for training and deploying models.
• Amazon Comprehend for NLP tasks.
• Amazon Rekognition for image and video analysis.
• Amazon Polly and Amazon Lex for speech processing.
• AWS Bedrock for 1-unified interface to all LLM models

Conduct regular scans of active AWS accounts to list all resources that could be linked to AI usage. Check for recently created or active resources that haven't gone through official approval channels.

Step 2: Analyze Data Access Logs for AI Model Activity

Use AWS CloudTrail logs and AWS CloudWatch to track data access patterns. Look for activities such as:

• High-frequency data pulls involving sensitive datasets. These might indicate data being processed or analyzed by AI models.
• Unusual patterns of data export or transfer, particularly if associated with services like SageMaker, which may suggest unauthorized model training or inference.

Identifying departments or individuals with significant data access, particularly to sensitive data sources, can provide insight into potential Shadow AI activities.

Step 3: Monitor Outbound API Calls and Data Movement

Many Shadow AI initiatives leverage third-party APIs, so monitoring outbound API calls can uncover unauthorized activity. For instance:

• Use AWS VPC Flow Logs and Network Access Control Lists (NACLs) to track outbound traffic.
• Set up alerts for high-frequency or high-volume API calls, particularly to external AI platforms or API endpoints.

Step 4: Conduct AI Model Audits

Regular audits can help identify unapproved models. Set up a monthly review process with stakeholders from IT, security, and departmental leads to audit all deployed AI resources. Establish a baseline to identify any new additions that haven't gone through official channels.

Conclusion

Shadow AI can introduce significant risks to your organization’s data security, compliance, and governance. By proactively managing AI resources, establishing strict policies, and leveraging AWS's security services, organizations can mitigate the risks associated with Shadow AI. Building a comprehensive governance framework with the right monitoring, detection, and enforcement tools can enable security teams and CISOs to tackle Shadow AI head-on, ensuring that AI innovation remains secure, compliant, and within the bounds of organizational policies.