Article
Shadow AI: The Hidden Risk and How to Manage It
November 14, 2024 ...
As AI tools grow more accessible and user-friendly, many employees are using these applications on their own—often without the knowledge or approval of IT teams. This trend, known as “Shadow AI,” poses a hidden risk for organizations. When employees turn to personal AI tools to boost productivity, they might unknowingly compromise data privacy and regulatory compliance, introducing security vulnerabilities.
To stay ahead, innovation teams need to provide approved, secure AI tools before employees seek alternatives. Here are three approaches to building a trustworthy AI ecosystem in your organization:
1. Vendor-Specific AI Solutions
Subscribe to a team or enterprise account with AI providers like OpenAI’s ChatGPT Enterprise or Microsoft CoPilot.
Pros: You get first-class, reliable AI solutions directly from industry leaders, ensuring consistent performance and strong security.
Cons: Limited flexibility, as these tools won’t integrate competitor models and aren’t fully customizable for unique business needs. To cover missing features, companies may need to enter into multiple enterprise subscriptions (like Perplexity.ai for Q&A or Midjourney for image generation). This approach can drive up costs per person, increase onboarding time, and create added management overhead for workspaces and billing.
Example Use: An organization can use ChatGPT Enterprise for secure report generation and meeting summaries, Perplexity.ai for market research, and Midjourney to generate images for presentations.
2. Model-Agnostic AI Hub for Teams
Use a centralized, vendor-agnostic hub, like Brancher One, designed for teams to build, share, and customize AI tools.
Pros: Brancher One allows flexibility by incorporating multiple AI models and updating regularly with the latest tools. It’s adaptable to team-specific needs and feature requests.
Cons: Replicating popular tools (like ChatGPT or Midjourney) may not offer the depth of features found in native applications.
Example Use: A marketing team uses Brancher One to access various AI models for text and image generation, empowering them with sanctioned tools that keep data secure.
3. On-Premises AI Deployment Using Open-Source Tools
Deploy an on-premise version of a generative AI tool with open-source models, using frameworks like Langchain.
Pros: You retain full control over functionality and data, ensuring maximum privacy and compliance.
Cons: Requires a high upfront investment and ongoing maintenance by skilled engineering teams, including servers, software, and support.
Example Use: A financial services firm with strict compliance needs could deploy an in-house ChatGPT alternative for customer support, keeping all data within its secure infrastructure.
Getting Started
By offering approved, accessible AI tools, you eliminate the need for employees to turn to unsupported applications, protecting sensitive company data and ensuring compliance. Start with a phased rollout, selecting high-impact use cases and gathering employee feedback to refine your approach.
With the potential benefits of AI, don’t let Shadow AI catch your organization off-guard. Which AI approach would your team benefit from most? Let us know!
Shadow AI—uncontrolled and unauthorized use of AI tools by employees—poses significant risks to a company’s data security, compliance, and operational integrity.
During a free online event on December 10th, Brancher.ai co-founder Elisha Terada will cover:
- Understanding Shadow AI
- The Importance of a Top-Down Approach
- Building an AI-Ready Framework
- How tools like Brancher One help you combat Shadow AI
- Case Studies and Real-World Examples
- Interactive Q&A Session
Empower your organization to harness the potential of AI while minimizing risks associated with Shadow AI. Sign up for the December 10th talk to see how you can create a safe and productive AI environment for your teams!
