What is shadow AI? A comprehensive guide

Shadow AI refers to the use or implementation of AI technologies that an organization’s IT department does not control or monitor. According to IBM, 30% of IT professionals report that employees have already adopted new AI and automation tools without IT oversight. But are these tools being used within secure governance frameworks?

The AI field is rapidly evolving, with open-source principles driving the release of new datasets, models, and products daily. This is especially evident with generative AI (GenAI), which can produce and process content at remarkable speeds and volumes. Many people are increasingly using GenAI tools, such as personal assistants, benefiting from their tailored experiences and optimized processes. For example, ChatGPT, a highly popular AI tool, reached 100 million weekly users within a year of its launch. OpenAI’s terms state that user conversations may be used for future model training unless users opt out.

The risk is that users might inadvertently share private and sensitive information, which could potentially be exposed or exploited. In response, Chief Information Security Officers (CISOs) and Chief Information Officers (CIOs) are drafting policies to address the use of tools like ChatGPT.

However, outright banning AI can lead to missed opportunities and may result in the rise of shadow AI. To effectively leverage AI’s business potential while managing security risks, organizations should promote safe and controlled AI adoption. Let’s explore shadow AI further and discuss strategies for balancing AI’s benefits with its risks.

Shadow AI vs. shadow IT

Governance differences: Data, code, and models in AI

While shadow IT risks can be managed through encryption, software development lifecycle (SDLC) policies, and automated monitoring of access and network usage, shadow AI presents different challenges. AI involves data, code, and models, with the latter being non-deterministic and harder to secure. Governance frameworks for AI are still evolving and under active research.

Adoption differences: Widespread use of AI

Shadow IT risks are typically confined to developers, a more controlled and homogeneous group with expertise in the technology they are using. In contrast, AI can be adopted by a broad range of users, many of whom lack knowledge of best security practices. This makes the attack surface for shadow AI broader and less defined compared to shadow IT.

Risks associated with shadow AI

Shadow AI introduces risks that match the breadth of its attack surface. Here, we explore the top three concerns:

Data protection

Shadow AI users might accidentally expose sensitive data or intellectual property while interacting with AI models. These models can be trained on user input, such as prompts given to large language models, potentially making this data accessible to unauthorized third parties. For example, Samsung employees input proprietary code into ChatGPT to streamline tasks, which could lead to that code being used in future AI model releases if users haven’t opted out of data collection.

Information integrity

Users of shadow AI may rely on misinformation generated by AI models. Generative AI (GenAI) models can produce incorrect or misleading information, known as "hallucinations," when uncertain about answers. For instance, two New York lawyers were fined $5,000 and faced reputational damage for relying on fictitious case citations generated by ChatGPT. Additionally, bias in AI training data can lead to biased outputs. For example, Stable Diffusion often generates racially and gender-biased images, such as depicting housekeepers predominantly as black women.

Regulatory compliance

Shadow AI lacks the auditing and monitoring needed to ensure compliance with regulatory standards. With new regulations like the EU AI Act and updated GDPR guidelines coming into effect, organizations must prepare to meet these evolving requirements. Non-compliance not only presents legal risks but can also harm a company's reputation and lead to significant financial costs, potentially exceeding those associated with shadow IT.

Benefits of addressing shadow AI

While shadow AI presents risks, managing it effectively can unlock significant benefits. Bringing shadow AI into the light allows organizations to leverage AI for enhanced process efficiency, personal productivity, and improved customer engagement. This advantage extends to all teams, including security and governance, risk, and compliance (GRC) teams. For instance, a security analyst could use a large language model to gain insights on handling a security incident not covered by the existing response plan.

Best practices for managing shadow AI

Organizations must evaluate their risk tolerance to develop an effective strategy for managing shadow AI. It’s about finding a balance between governance and accessing AI's benefits. Here are some approaches:

Total Ban

Banning AI completely eliminates risks but also forfeits all associated benefits. This strategy requires no governance but misses out on AI’s potential advantages.

On-premises AI solutions

Adopting on-prem AI solutions provides full security control and reduces governance needs since third-party systems are excluded. However, this approach can limit AI benefits due to the substantial time and resources required for implementation.

Unrestricted AI adoption

Allowing unrestricted AI use without governance means individuals must self-regulate to manage security risks. While this approach may foster agility and speed, it lacks safety guarantees and can lead to potential vulnerabilities.

Incremental adoption with governance

The most balanced approach is to implement AI incrementally within agile governance processes. This method ensures that organizations can quickly and securely reap the benefits of AI while managing risks effectively.