Fighting Against the Rise of Shadow AI

Dataiku Product, Scaling AI, Featured David Talaga

In a matter of months, Generative AI has spread throughout businesses like wildfire, gaining such momentum that it has become — for some — challenging to control. But the truth is, the adoption of Generative AI is just the tip of the “shadow AI” iceberg. 

As AI projects proliferate and organizations progressively shift from the experimentation phase to the production phase, the risks of losing control are very real. According to Forrester, in 2024, shadow AI will spawn as organizations struggling to manage regulatory, privacy, and security issues won’t be able to keep up with widespread user-led AI. 

Gartner has raised similar concerns, positioning AI security and risk management as one of the top priorities for 2024. So what steps can we take to effectively fight the rise of shadow AI?

shadow AI

What Is Shadow AI Exactly?

Shadow AI can be described as the uncontrolled proliferation of various user-led products derived from generative or predictive AI within an organization. This includes (but is not limited to) predictive analytics, models, AI-powered applications, and the use of large language models (LLMs) without adhering to the transparency and visibility criteria set either by the company itself or by regulations in place.

What Are the Risks Inherent to Shadow AI?

Uncoordinated AI implementations may create integration challenges with existing systems and infrastructure, hindering interoperability between systems. Plus, the absence of standardized AI tools can lead to inefficiencies, increased IT complexity, and difficulties in scaling AI initiatives across the organization.

Like with classic predictive machine learning, black-box models are a challenge. The black-box nature of many Generative AI models therefore poses additional hurdles in understanding decision-making processes, raising transparency issues. 

In addition, the potential for misuse or bias in AI models underscores the importance of implementing robust ethical guidelines and governance frameworks to mitigate unintended consequences and ensure responsible Generative AI deployment. AI that is happening outside of those frameworks puts the business in jeopardy.

Lastly, and most obviously as this has been widely reported in the press, there is a potential loss of confidential data, as some advanced Generative AI models may inadvertently generate outputs containing sensitive information without proper control. 

How Do You Prevent Shadow AI?

Since the risks of Shadow AI are undeniable, proactive measures are imperative. Fortunately, Dataiku has recently rolled out three robust capabilities crafted to counteract inadequately controlled AI practices. 

#1 LLM Mesh to Gain Control and Visibility Over LLMs

The LLM Mesh acts as a crucial barrier against the infiltration of shadow AI, employing routing and orchestration to ensure secure AI flow and usage. It enables organizations to efficiently build enterprise-grade applications while addressing concerns related to cost management, compliance, and technological dependencies. It also enables choice and flexibility among the growing number of models and providers.

To tackle the financial challenges of LLM usage, the LLM Mesh can provide cost reports, providing insights into usage and investmentsI. The LLM Mesh also monitors data flow for potential personal identifiable information (PII) breaches and offers content moderation capabilities, training LLMs to identify harmful outputs.

#2 Unified Monitoring to Supervise Every AI Model Regardless of Its Origin 

Standardizing metrics, ensuring model health, and handling project scalability are key concerns for any organization wishing to operate AI at scale and get rid of shadow AI. But integrating monitoring data about model activity, deployment, and execution from third parties and presenting it in a user-friendly interface is often too much effort for ops and data science teams.

By automatically aggregating multiple types of monitoring in a single place (activity, deployment, execution, model), Unified Monitoring in Dataiku acts as a central cockpit for all MLOps activity in one place.  It becomes your one-stop solution for tracking the health of AI models across diverse origins, from projects and APIs to cloud endpoints like AWS Sagemaker, Azure Machine Learning, and Google Vertex AI. 

#3 AI Governance to Ensure Long-Term Control, Federation, & Compliance 

Setting up approval workflows across the AI value chain is critical to reduce risk and ensure accountability. Dataiku, for example, facilitates the creation of clear steps and gates to explore, build, test, deploy, and maintain AI projects.

This comprehensive oversight not only facilitates tracking the use of AI across the organization but also empowers decision makers to discern whether the implementation demands an additional layer of dedicated governance to address emerging challenges effectively. 

In the context of pre-trained LLMs, where risks concerning accuracy, unknown biases, and data leaks become paramount, the significance of vigilant AI governance amplifies. Dataiku AI Governance capabilities have continuously evolved to now include oversight of Generative AI models. Dataiku Govern plays a pivotal role by equipping users with a single pane of glass with the ability to accurately  oversee the deployment of LLMs across various analytics and AI projects.

From Risk Mitigation to Virtuous Effect

From productivity gains to information democratization and the emergence of AI-powered data products  (whether predictive, prescriptive, or generative), users have enthusiastically embraced AI. This is an amazing achievement and worlds away from the pre-ChatGPT era, with people across the business recognizing its application and benefits. 

Nevertheless, AI democratization must not neglect necessary oversight, encompassing costs, usage, and processes. This also involves the necessary training and empowerment of AI front line users, avoiding bring your own (BYO) AI effects. 

"By 2026, organizations that operationalize AI transparency, trust, and security will see their AI models achieve a 50% improvement in terms of adoption, business goals, and user acceptance."* This Prediction from Gartner serves as a reminder that, above all, the goal is not just to combat shadow AI but to establish a virtuous cycle that leads to long-term value creation.

*Tackling Trust, Risk, and Security in AI Models

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