Prevent Shadow AI from Casting a Shadow on Your Organization
From healthcare to financial services to the federal government and beyond, AI has exploded on the scene as a tool that promises organizations greater efficiency, productivity, and cost savings.
But without guardrails around adoption, application, and accountability, organizations remain vulnerable to security breaches and data compromise—particularly within industries that handle large volumes of sensitive information.
The problem is twofold:
- Excitement for what AI can do eclipses concern for the harm AI might do.
- AI evolution outpaces AI regulation, leading to vulnerabilities and blind spots.
It’s a perfect storm for individual employees (or entire departments) to go rogue in adopting and using AI agents, leaving companies at risk for the proliferation of shadow AI.
Shadow AI: What It Is and Why It Happens
What is Shadow AI?
The “shadow” part refers to the implementation of tools, applications, or platforms—by employees or teams—without the knowledge or approval of departmental or organizational leaders.
As Jason Hunt, IntePros Chief AI Officer, explains, “It happens in the dark, away from scrutiny, and it can be deliberate or unintentional. Either way, it presents a serious security risk—particularly in cases where there’s still a lack of fundamental understanding about how a company’s proprietary data is managed and protected.”
Why Does Shadow AI Occur?
A lack of regulation for AI as a whole, and within specific industries, has opened the door for a “Wild West” approach to AI adoption.
AI has evolved faster than those responsible for its oversight (especially at the highest levels) can reasonably regulate it. As a result, overarching restrictions around what tools can be used, how they can be used, and who has permissions to use them are slow in coming.
Jason explains, “Without these guardrails, organizations are left to create standard operating procedures on their own, leading to a wide variation in the degree to which AI is monitored. This leaves users free to try the latest programs without truly understanding how data can be exposed.”
It also makes it easy for workers to exploit instances where rules are vague, and they are eager to improve workflows and clear leadership hurdles.
The Top Risks of Shadow AI
Why is shadow AI such a security concern? Three reasons:
- Easy access. AI applications are widely available and easy to use.
- Uncontrolled exposure. Individuals are most familiar with using open-source AI platforms (e.g., Chat GPT) within the public domain.
- Blind faith. Many willingly trust that AI is secure, yet no one can 100% guarantee data is not being leaked into other AI models.
Without official checks and balances, the efficiencies gained by using AI (especially in secret) can come at a steep price: compromise of critical data leading to severe repercussions.
Data Manipulation
Many fail to realize that common AI tools (especially open-source, generative models) leave data open to manipulation from any entity anywhere.
These platforms must be trained on data to consistently perform better over time. The more useable the data is from the start, the more meaningful the output is in the end. Unfortunately, many organizations use proprietary, often sensitive data for “training.”
This data is at increased risk of being altered in a way that fundamentally changes AI interpretation, manipulating output to result in detrimental consequences to a company and its clients.
Intellectual Property Exposure
In today’s upended market, no company can afford to lose competitive advantage. But if internal employees or teams are using AI as they choose, that’s exactly what can happen.
Your organization risks exposing its intellectual property, for free, to the masses.
Emails, SharePoint content, internal memos—all and more are fair game as potentially scrapable information. Gone are the years (sometimes decades) spent refining your differentiators and capitalizing on proprietary advantages.
Data Breach
Digital sharing of information always comes with the risk of a data breach. If you’re in a highly regulated environment responsible for handling sensitive data, you have more at stake and more to lose.
If someone in your organization implements an AI tool that results in a regulatory violation (e.g., SEC, HIPAA), ethical breach, or accidental leak of personally identifiable information, you’ll likely face fines, sanctions, lawsuits, and even jail time.
6 Ways to Lessen the Impact of Shadow AI
AI developers are eager to strike while the iron’s hot.
According to Jason, “Right now, we’re seeing a flood of quickly developed, low-cost tools enter the market. Many are nothing more than an Open AI backend in a fancy wrapper, and most prioritize profit over security.”
Here are five things you can do today to prevent or curb the impact of shadow AI:
- Understand Your Architecture. Have your engineers and CTO walk through your existing architecture to understand all data AI has access to and how it’s storing it, to identify areas where potential exploits exist. If operating in a cloud computing environment (e.g., AWS, Google, Microsoft Azure), there are additional precautions necessary to ensure security.
- Containerize Your Information. To ensure information is not being shared with external entities or large language models, contain it within your organization. This includes setting controls around levels of access, so that a junior accountant does not have access to the same type and depth of information as the CFO.
- Develop Specialized AIs. Invest in development of proprietary, company-specific AI tools. Create these AIs in a way that is focused on individual use cases versus an overarching program with access to every detail of your organization.
- Avoid External APIs. Be sure any AI you’re using is not dependent on, or calling out to, external entities. Work within secure enclaves, off the internet, and only with products that have authority to operate, which can be determined via scans to identify suspicious ports, IP addresses, or code breaks.
- Establish Clear Roles and Privileges. AI doesn’t differentiate between users, so you must. Assign AI user levels and access permissions based on roles and the data they need to successfully accomplish their work.
- Develop Legally Sound Operational Policies. Create and communicate clear corporate policies to all employees around how AI is intended to be used—when it is acceptable, when it is now allowed, what tools are permitted, and guidelines for securely using it.
How IntePros Can Help
To stay on top of the latest AI advancements and security protocols, it takes specialized, skilled talent. Connect with the experts at IntePros today to engage to- tier AI professionals— engineers, data scientists, and researchers. Let’s put your business at the very front of the AI evolution.
