Understanding Shadow AI: Key steps to Protect your Business

Artificial Intelligence (AI) has become deeply integrated into daily life, with 55% of Americans reporting daily interactions with AI and 27% engaging with it almost constantly, according to Pew Research. However, this widespread adoption extends to workplaces, often without employer awareness, leading to a phenomenon known as Shadow AI.

While some instances of Shadow AI may enhance productivity by accelerating workflows, they also introduce significant risks. Employees, despite good intentions, may inadvertently expose their businesses to privacy and security threats. This risk has been magnified by the rapid adoption of Generative AI (GenAI), which has already prompted regulatory actions, such as the EU’s AI Act passed on March 13, 2024.

Shadow AI represents one of the most pressing challenges for enterprises planning AI deployment and integration in 2024. These organizations aim to incorporate AI models securely and transparently, yet Shadow AI can undermine these efforts, compromising security, ethics, and compliance. The concept echoes the rise of shadow IT a decade ago, where unsanctioned cloud applications posed significant concerns. Now, the proliferation of GenAI, capable of generating written or visual content from text prompts, has given rise to Shadow AI, creating a new spectrum of organizational risk.

Also Read: Unlocking Hyperautomation: What It Takes to Guard Quality with AI

What does Shadow AI Mean?

Shadow AI refers to the unauthorized use of AI tools by employees without the knowledge or consent of their company. This clandestine utilization means companies are often unaware of the AI activities occurring within their operations. While such AI use may enhance productivity by accelerating task completion, the lack of visibility and established guidelines presents significant risks. Without oversight, controlling the outcomes of AI applications becomes challenging, posing a threat to the company’s integrity and operational success.

Evidence indicates that Shadow AI is a growing concern across various industries, even though it has not yet resulted in widely publicized security failures. Many tech companies often do not disclose hacks or breaches, exacerbating the potential dangers of unmonitored AI use.

Shadow AI vs. Shadow IT: Understanding the Difference

Shadow AI refers to the unauthorized use and incorporation of AI tools within organizations without the knowledge or approval of the central IT or security functions. In workplace settings, this can involve employees accessing generative AI (GenAI) platforms or large language models (LLMs) such as ChatGPT to perform tasks like writing code, drafting content, or creating graphics. Although these activities may seem harmless, the lack of oversight by IT departments means businesses are at increased risk of exploitation and potential legal issues.

Shadow IT, on the other hand, occurs when employees build, deploy, or use devices, cloud services, or software applications for work-related activities without explicit IT oversight. The growing accessibility of various SaaS applications has made it easier for users to install and use these tools without IT involvement. The Bring Your Own Device (BYOD) trend further exacerbates this issue, as security teams may struggle to monitor the services or apps on personal devices and implement necessary security protocols.

Threats Associated with Shadow AI

• Risks to Data Security: Shadow AI can pose severe security issues. Employees who use AI tools outside the organization’s set security standards may make critical data vulnerable to hacking, exposing the data to probable breaches and resulting in legal complications from non-compliance.
• Quality Control Issues: In the absence of control, the quality of AI algorithms and models is likely to be as good as bad. This might lead to less accurate insights or decisions that are detrimental to business operations.
• Operational Inconsistencies: Shadow AI initiatives may bring in inconsistencies within the process flows across different departments or teams that retard collaboration among employees and create confusion.
• Dependence on Ungoverned Tools: When using unapproved AI tools, the dependency is on unsupported or obsolete software, which heightens the risk of system crashes, compatibility issues, and problems in maintenance.
• Legal and Compliance Risks: The use of unauthorized AI can result in a breach of industry regulations or legal requirements, thereby exposing the organization to potential litigation, fines, or reputational harm.

Also Read: Optimizing AI Advancements through Streamlined Data Processing across Industries

Strategic Steps to Gain Control of Shadow AI in the Organization

1. Discover and Catalog AI Models

Identify all AI models in use across public clouds, SaaS applications, and private environments, including shadow AI.

• Catalog AI models in both production and non-production environments.
• Link data systems to specific AI models and computing resources to applications.
• Gather comprehensive details about AI models in SaaS applications and internal projects.

2. Assess Risks and Classify AI Models

Align AI systems with risk categories outlined by global regulatory bodies, such as the EU AI Act.

• Provide risk ratings for AI models using model cards, covering toxicity, bias, copyright issues, hallucination risks, and model efficiency.
• Use these ratings to decide which models to approve and which to block.

3. Map and Monitor Data and AI Flows

Understand the relationship between AI models and enterprise data, sensitive information, applications, and risks.

• Create a comprehensive data and AI map for all systems.
• Enable privacy, compliance, security, and data teams to identify dependencies and potential risks.
• Ensure proactive AI governance.

4. Implement Data and AI Controls for Privacy, Security, and Compliance

Secure both input and output data for AI models to mitigate risks.

• Inspect, classify, and sanitize all data flowing into AI models using masking, redacting, anonymizing, or tokenizing.
• Define rules for secure data ingestion in alignment with enterprise policies.
• Deploy LLM firewalls to protect against prompt injection attacks, data exfiltration, and other vulnerabilities.

5. Ensure Regulatory Compliance

Implement comprehensive, automated compliance with global AI regulations and frameworks, such as the NIST AI RMF and the EU AI Act.

• Define multiple AI projects and verify the required controls for each project.
• Maintain up-to-date compliance with an extensive list of global regulations.

What is the Future of Shadow AI?

Generative AI has several use cases across various industries, offering efficiency and performance improvements. However, the future of this technology is uncertain, and most businesses will look to introduce AI solutions that can drive better results. Effective planning for this technological shift will be in the areas of digital transformation and security. Generative AI might be misused over the internet because, in reality, it bypasses traditional mechanisms of oversight.

But with the current hype around AI, the risk of shadow AI only increases. And that: People will make use of AI-driven tools for ends that may have nothing to do with any goals or security of an organization. However, there are also solutions to meet some of those challenges. An organization should device an all-rounded strategy that will involve technical controls, staff surveys, effective onboarding processes, stringent policy enforcement, and user education to minimize the risks that come with the illicit use of AI. The proper planning and execution will let organizations empower the potential of the benefits that AI has to throw around and keep its pitfalls at bay.

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