AI Compliance Blueprint: A Step-by-Step Guide for GRC Teams to Safeguard Sensitive Data

Article Summary:
The article provides a detailed guide for compliance teams to establish a secure and compliant AI framework, helping organizations safeguard sensitive data and avoid regulatory pitfalls. It emphasizes proactive risk assessments, clear AI usage policies, enforcement strategies, employee training, and continuous monitoring to mitigate AI-related data breaches.

AI Compliance Guide Key Insights:

• Conduct thorough AI risk assessments to map data flows and regulatory exposure (GDPR, HIPAA, CCPA, etc.).
• Implement strict AI compliance policies, including tool whitelisting, granular access control, and clear usage guidelines.
• Enforce rules with continuous monitoring from automated SSPM tools, network restrictions, regular risk assessments, policy enforcement, and AI-generated content reviews.
• Train employees with real-world simulations and practical modules to reduce human error risks.
• Continuously monitor AI tool usage for new risks, and update policies to adapt to evolving AI technologies and regulations.

When it comes to data, a slight misstep can have your organization plastered across headlines for all the wrong reasons. 

Every year, we see thousands of multi-million dollar fines and “we take security seriously” PR statements that no one believes. But behind the scenes, it’s rarely outright negligence but a false sense of security that everything was under control. 

If you’re part of a GRC team, you need a solid AI compliance policy that doesn’t crumble under scrutiny. This guide will walk you through the exact steps to keep security breaches at bay.

Steps to Building a Secure and Compliant AI Framework

A secure AI framework builds compliance into the company’s foundation. This way, every line of code and data point plays by the rules before trouble strikes. Here’s how you can pull this off. 

Step 1: Conduct an AI Risk Assessment

Before you start deploying AI tools across your organization, you need to understand where your sensitive data flows and where you might be walking into regulatory quicksand.

Identify Where AI Interacts with Regulated Data

To begin with, identify every spot where AI comes across sensitive information like personal and financial data. 

Letting your marketing team use ChatGPT to design email campaigns may sound both harmless and efficient– and it may very well be. But what if they’re pasting in reports and documents to help improve targeted messaging? Those documents or content may include customer email addresses or behavioral data, and marketers are notoriously ill-versed in data security best practices, since it’s not their technical practice area. 

This would be a clear instance of personal data exposure. Put simply, compliance laws like GDPR and CCPA will not grant an exception to compliance rules simply because the marketing team didn’t realize they were violating compliance requirements. Requirements and penalties exist whether all teams with access to data are aware of these regulations or not. At the same time, your teams are under tremendous pressure to utilize AI and speed up processes.

Here’s what you can do. 

• Determine which AI applications are currently connected to your cloud workspace, including teams’ shadow IT apps, extensions, and AI personal assistants.
• Figure out where AI is pulling data from (which specific files or folders) and their security and business governance classifications of the data inside.
• View a real-time access list of the users with access to AI applications.
• Monitor for risks such as–
  • AI applications with access to sensitive files
  • Applications that have known risks 
  • Privileged users who may be employing AI applications or extensions to help them perform routine tasks with sensitive data.
• Survey your employees. Ask them where AI tools fit into their daily tasks to help surface shadow AI. Sometimes, risks lurk in unexpected places.
• Work with IT to track data movement. Just because AI can analyze certain data doesn’t mean it should.

OR

Use a real-time SaaS monitoring and automation tool to help…

• Gain immediate visibility into shadow AI / IT (including apps that end users may not have realized are sending data to external AI engines in order to perform routine tasks).
• Control AI applications’ access to sensitive data.

Enforce users’ compliance with AI acceptable use policies once you create them.Next: Assess Legal and Compliance Exposure

If your company collects European customer data, GDPR’s Article 35 may require a Data Protection Impact Assessment (DPIA). 

Meanwhile, if AI touches health data, HIPAA (Health Insurance Portability and Accountability Act) has strict regulations. 

Likewise, if you’re operating in California, the CCPA (California Consumer Privacy Act) means customers can demand to know (and delete) their AI-processed data.

Ideally, consider:

• Partnering with your legal team early to avoid a compliance crisis.
• Identifying which regulations apply to your AI operations (GDPR? HIPAA? CCPA? PCI DSS?)
• Put risk protections in place before deploying AI. Implementing encryption or stricter user permissions could stand between you and a compliance nightmare.
• Auditing regularly, and never assume AI handles data “correctly” out of the box. 

Step 2: Create a Clear AI Compliance Policy

If employees don’t know exactly what they can and can’t do with AI tools, someone will eventually feed sensitive data into a chatbot, and you’ll deal with issues later. 

A solid AI compliance policy should highlight what’s allowed and what happens if someone ignores the rules. 

Define What’s Banned vs. Allowed

Not all AI tools are created equal. Some are data traps. In other words, once the information goes in, you lose control. Others are built for secure enterprise use. 

Your policy needs to spell out which tools employees can use and which are off-limits.

Ban: AI tools that store user inputs for training. That means public-facing AI tools like ChatGPT’s default mode, which retains conversations to improve its model. If an employee pastes confidential client data into it, that data is no longer private. It’s now part of OpenAI’s system, which clearly violates most privacy laws.

Allow: Enterprise AI tools that guarantee encryption and non-retention policies. Think Salesforce Einstein GPT or Microsoft Azure AI, where companies maintain data ownership. 

As a rule of thumb:

• Create a whitelist of approved AI tools. If it’s not on the list, employees can’t use it.
• Set clear rules on AI-generated content. Can employees use AI for customer emails or coding? Mention it. 
• Define red zones. Sensitive data like financial records or trade secrets should never touch an AI model outside your control.

Create a Policy That’s Easy to Understand

A policy is useless if no one reads (or understands) it. So, ditch the legal jargon. Here’s a snippet of what an effective policy might look like:

 “Employees must not input confidential or customer data into public AI tools (e.g., ChatGPT, Google Gemini, or Bard). Approved alternatives include [list of enterprise tools]. Violations of this policy may result in disciplinary action, including [list of consequences]. Regular audits will be conducted to ensure compliance.”

Step 3: Enforce AI Compliance

You can tell employees not to feed sensitive data into AI tools, but if there’s nothing stopping them, someone will eventually do it (intentionally or not). 

That’s not because they’re reckless but because AI makes things so easy that a quick copy-paste can feel unproblematic. Here’s how to avoid that. 

Deploy Data Loss Prevention (DLP) Tools

Tools like Symantec DLP or Forcepoint monitor, detect, and block any attempt to share sensitive data with unauthorized AI platforms. 

So, even if someone tries to paste customer PII or financial records into an AI chatbot, these tools flag and block the action in real-time.

Restrict Access to Public AI Tools with Network Blocks or SSPM

If an employee can’t access risky AI platforms, they can’t misuse them. 

IT teams should use firewalls and proxy controls to block access to public AI tools like ChatGPT’s free version, Google Gemini, and other AI platforms that don’t meet compliance standards.

You can work with your IT team to restrict network access to non-compliant AI platforms they know about, but to close gaps in visibility it’s best to use an SSPM tool that provides continuous security monitoring, visibility into shadow AI apps or extensions, and comprehensive policy enforcement across your environment.

Conduct Quarterly AI Tool Reviews

Every three months, compliance teams should review AI tool usage logs (or alert logs if you are using SSPM) to identify unauthorized activity and coach users again in security best practices for AI governance. This means checking:

• Which AI tools employees are using
• Whether employees are feeding sensitive data into these tools
• Any emerging compliance risks that need new policies 

Spot-Check AI-Generated Content

Once you solve for immediate security risks, it’s time to solve for the business risks of AI use.

AI-generated content might look polished, but that doesn’t mean it’s compliant or even ethical. The thing is, AI doesn’t understand context the way humans do. It generates responses based on past patterns. 

That means even if you limit a private AI model to privileged users who are permitted to access, view, and leverage privileged data, things can still go sideways. For example, an AI-written report could subtly misinterpret financial data, or an AI-generated customer email might contain misleading claims that increase corporate liability. The only way to prevent AI from slipping up is to spot-check its output critically and often.

It’s also helpful to create a risk matrix for AI-generated content. This means reviewing high-risk outputs every single time. Secondly, consider setting up internal review checkpoints before AI content is published or sent externally. 

Step 4: Train Employees on AI Risks

According to the Society for Human Resource Management (SHRM), human error is  the number one cause of data breaches (54%). 

You can have the tightest firewalls and the most advanced AI policies, but all it takes is one employee to paste confidential data into an AI chatbot to trigger a data breach. 

That’s why your employees need hands-on experience and active testing to understand the stakes.

AI Risk Mitigation Simulations

To help users develop better data security DNA when it comes to AI use, walk through a few real-world use cases with them to help them understand not only the risks but how to address them. Most employees are more than happy to engage and help build a culture of security when they are approached in a positive, collaborative way. Punitive approaches or scare tactics tend to have the opposite effect, reducing end users’ engagement in building a culture of security. So, it’s important to focus on training them as your front-line in the overall mission of implementing AI securely. After all, they are highly motivated to make sure they can continue to use apps and extensions to speed up daily tasks!

Here’s how it works:

• Engage each team separately, so you can tailor examples to their daily work tasks.
• Perform a sample task much like the ones on their own to-do lists every day.
  • EX) For a financial team, walk through how they might use an AI application to automatically generate executive insights from a report or dashboard. For a marketing team, walk them through the process of turning internal data into polished marketing content.
• Explain what the model is doing with the data on the back end in order to quickly generate outputs, including a diagram that shows data flows and everyone else (internal or external) who will also have access to the same AI model.
• Review the risks associated with potentially exposing data in LLM models for non-privileged, or even unauthorized users.
• Review the outputs with your team, walking through each new risk and how it impacts both your corporate security posture and your compliance standing.
• Ask them what they would suggest as a way to mitigate these risks.
• Listen to answers thoughtfully, engaging end users in the process, and affirm any good ideas or observations they have, educating them on data governance.
• Show them any processes or tools you wish to implement in order to support policy enforcement, explaining that this is a way to speed up, automate, and increase consistency of data governance across the organization.

Training Modules That Actually Work

Most AI risk training is too vague. Employees need training that is directly tied to their daily tasks.

Here’s what every employee must know. 

• AI tools store data even when they claim they don’t. Many AI tools (like ChatGPT’s free version) retain user inputs to improve future responses. That means every confidential detail typed into an AI chatbot could be saved and later retrieved by the provider.
• “Private mode” doesn’t mean “safe mode”. Some AI tools offer “no training” or “private” settings but these often still store session data temporarily. 
• There’s no “undo” button for AI leaks. Unlike an email sent to the wrong recipient, data shared with AI tools can’t be recalled because the company doesn’t control it.

Step 5: Monitor and Iterate

If your company isn’t actively mitigating AI risk, you’re flying blind. The right metrics can reveal what’s working and what needs immediate action. Here are a few things you may consider.

Shadow AI Reduction

Shadow AI, the use of AI tools without IT approval, is one of the major compliance risks. 

Make sure you track Track AI-related network traffic to detect unapproved AI usage. Besides, consider using combined SSPM (SaaS Security Posture Management) – DLP (Data Loss Prevention) tools to surface shadow AI and prevent unauthorized AI interactions.

Track Compliance Failures

If nobody is reporting AI-related security incidents, it doesn’t mean they’re not happening. It just means your employees aren’t aware of them or are perhaps afraid to report them. Coming back to the need to take a collaborative approach lined with opportunities for positive reinforcement, remember that adults thrive in environments of positivity.

A well-run AI compliance program is likely to see an initial increase in incident reports as employees learn to recognize risks. Over time, as training and end user muscle memory improves, those reports should start to decline. Ultimately, the goal here is self-correction with the backstop of tools and continuous monitoring, helping you further build out your layered security strategy.

Consider:

• Logging how many AI-related security incidents are reported each quarter.
• Identifying which departments struggle the most with AI compliance and need additional training.

Update Your AI Policy

The AI tools your employees trust today might become your security risks tomorrow. Just look at how frequently Microsoft Copilot or ChatGPT Enterprise change their privacy settings and security certifications.

Revisit AI guidelines every six months to reflect the latest regulations. Update your SSPM, compliance enforcement policy settings to align with these updates. 

Finally, be sures your IT and compliance teams use automated tools to continuously assess risks associated with emerging AI tools. The way technology accesses and processes data in the age of AI is changing faster than ever before. We must prepare for the unexpected. Conclusion

AI is actively improving how your business operates right now. If GRC teams treat AI compliance as just another “requirement to check off”, they’ll never get past the point of reactivity to focus on proactive, compliant uses for AI..

Real AI governance requires you to build a system where innovation and security go hand in hand. You must give your employees the tools they need while making sure they don’t accidentally feed your sensitive data into OpenAI.

Keeping your data secure shouldn’t feel like a never-ending battle. With SpinOne, you get real-time visibility and the control you need to stop leaks before they happen. After all, hoping for the best won’t keep your data safe.