AI is reshaping security practices across industries. According to the IBM Global AI Adoption Index, 42% of enterprises have implemented AI solutions, with 40% actively exploring adoption. In this article, we’re tackling one of the first use cases of AI in enterprises: Microsoft Copilot—a game-changer in enterprise technology that is revolutionizing how organizations retrieve and use data. However, along with its undeniable power comes a hidden risk: oversharing information.
Generative AI isn’t just hype—it’s redefining how we work. And one of its most compelling applications is enterprise search. Imagine asking a single question and having an AI dig through countless emails, documents, and databases to deliver exactly what you need. It’s no surprise that tools like Microsoft Copilot are leading the charge, offering organizations an efficient way to make sense of their data chaos.
But here’s where it gets tricky: with great power comes the potential for TMI (too much information).
Picture this: a team member asks Copilot for “recent payment disputes” and inadvertently gains access to sensitive data containing customer credit card information. Yikes!
Why could this happen? Let’s break it down.
Enterprise search tools like Microsoft Copilot are like having an AI-powered librarian for your organization. But instead of being stuck in a dusty archive, this librarian is turbocharged with the ability to:
● Search Across Diverse Systems
Whether it’s your CRM, email, or cloud storage, Copilot retrieves data from multiple sources seamlessly.
● Understand Your Intent
Forget about exact keywords. Copilot uses natural language processing to grasp what you mean, not just what you type.
● Summarize and Synthesize
Instead of handing you a pile of files, it gives you digestible answers or summaries, saving you hours of reading.
It’s not just about finding information; it’s about making it actionable. This is why tools like Copilot are indispensable—but it’s also where they become a double-edged sword.
Behind the magic of Microsoft Copilot lies a Retrieval-Augmented Generation (RAG) model, a sophisticated GenAI architecture that combines:
● Data Connection
Copilot is plugged into your organization’s ecosystem—emails, SharePoint, OneDrive, Teams, CRMs and more.
● Retrieval Layer
When you ask a question, Copilot identifies the most relevant data sources and fetches the required information. It has access to all of the infomration it’s connected to.
● Generative AI
It then processes this data, generating concise responses or summaries tailored to your query.
Think of it as a relay race: your question triggers a search across all connected systems, retrieves data based on relevance, and passes it to the model to craft the answer.
Here’s the twist: enterprise search is only as secure as the data it’s connected to. Most organizations, unfortunately, have some degree of data misconfiguration—think files with incorrect permissions, overly broad access, or outdated restrictions.
Now, combine this with RAG models like Microsoft Copilot. These tools don’t just search—they embed your data’s permissions and accessibility rules into the AI model itself. That means:
The result? Oversharing information becomes a real concern:
● False Sense of Security
Misconfigured permissions might allow sensitive data to slip through the cracks. Users can unintentionally craft queries that extract data the AI “knows” but shouldn’t reveal.
● Data Exfiltration
Employees with ill intentions—or even curiosity—can exploit the system by carefully phrasing prompts to access restricted data.
● “Just a Question Away”
Unlike traditional tools, where accessing sensitive information might involve several hoops, generative AI simplifies the process. A well-crafted prompt is often all it takes to expose confidential details.
These issues can result in significant risks, including sensitive data exposure, compliance violations, and data leakage.
So, how do you balance the incredible productivity gains of tools like Microsoft Copilot with the real risks of oversharing and regulatory exposure? It’s no secret that data misconfiguration is inevitable, and even the best data governance programs can take years to mature—if they ever fully do.
Still, you can’t afford to wait. Generative AI’s transformative power is too valuable to shelve indefinitely. The solution lies in assessing your initial risks and continuously monitoring Copilot’s usage to detect and respond to potential security issues.
Here’s how:
As highlighted earlier, oversharing risks aren’t just theoretical—they’re happening today. Remember the example where Copilot inadvertently exposed PCI data during a simple query? Many enterprises recognize this danger and hesitate to roll out Copilot organization-wide until they fully understand the implications.
To manage this, you need to evaluate your risk before deployment:
● Simulate Real-World Scenarios
Test what Copilot might retrieve when handling sensitive queries. For example, could it surface data related to PCI or customer payment disputes?
● Automate Your Benchmarking
Instead of manually assessing potential risks, leverage automation tools to evaluate your data configuration against best practices. This gives you a clear understanding of where sensitive data might be exposed and helps you determine what to address and prioritize before scaling usage.
Once Copilot is deployed, the key to safe usage is visibility, monitoring, and automated remediation:
● Gain Visibility
Identify all data sources connected to Copilot and track what’s being accessed during queries. Know whether sensitive data is inadvertently being retrieved.
● Monitor Prompt Activity
Analyze prompts and responses in near real time. Assess whether sensitive data is being consumed by unauthorized users and whether it poses a risk to compliance or privacy.
● Automate Remediation
Oversharing incidents often stem from outdated configurations or overly broad permissions. Instead of manual fixes, automate the process of updating permissions and re-embedding them into the model.
● Apply Your Organization’s Policy
Ensure Copilot aligns with your enterprise data security policies and compliance standards by enforcing data governance and access control policies. Automate enforcement to continuously mitigate risks in Copilot interactions.
To ensure your team can manage risks effectively without adding unnecessary overhead, integrate Copilot monitoring into your existing security ecosystem:
● Connect to Your SIEM
Route events like data misconfigurations or oversharing incidents directly into your security information and event management tools for centralized tracking.
● Leverage ITSM Workflows
Automate ticket creation and resolution for incident response teams, helping them address misconfigurations or permission issues efficiently with different stakeholders and data owners.
● Enhance with Automation Tools
Use workflow automation to remediate permissions, fix misconfigurations, connect with data owners to perform permission fixes, and ensure updates are applied seamlessly, reducing the burden on your security and IT teams.
Generative AI tools like Microsoft Copilot are transforming the way organizations work, delivering incredible productivity gains. But with great power comes great responsibility—particularly when it comes to safeguarding sensitive data and maintaining compliance.
By taking proactive steps, such as assessing your initial risk, implementing continuous monitoring, and seamlessly integrating remediation workflows with your existing security tools, you can harness the full potential of Copilot without fear of oversharing or regulatory pitfalls.
At Opsin, we specialize in helping organizations like yours roll out Microsoft Copilot securely from day one. Our solutions ensures your deployment is both powerful and protected from day one.
Ready to unlock the full potential of Copilot while staying secure? Schedule a demo with us today to see how Opsin can help you achieve it.
AI is reshaping security practices across industries. According to the IBM Global AI Adoption Index, 42% of enterprises have implemented AI solutions, with 40% actively exploring adoption. In this article, we’re tackling one of the first use cases of AI in enterprises: Microsoft Copilot—a game-changer in enterprise technology that is revolutionizing how organizations retrieve and use data. However, along with its undeniable power comes a hidden risk: oversharing information.
Generative AI isn’t just hype—it’s redefining how we work. And one of its most compelling applications is enterprise search. Imagine asking a single question and having an AI dig through countless emails, documents, and databases to deliver exactly what you need. It’s no surprise that tools like Microsoft Copilot are leading the charge, offering organizations an efficient way to make sense of their data chaos.
But here’s where it gets tricky: with great power comes the potential for TMI (too much information).
Picture this: a team member asks Copilot for “recent payment disputes” and inadvertently gains access to sensitive data containing customer credit card information. Yikes!
Why could this happen? Let’s break it down.
Enterprise search tools like Microsoft Copilot are like having an AI-powered librarian for your organization. But instead of being stuck in a dusty archive, this librarian is turbocharged with the ability to:
● Search Across Diverse Systems
Whether it’s your CRM, email, or cloud storage, Copilot retrieves data from multiple sources seamlessly.
● Understand Your Intent
Forget about exact keywords. Copilot uses natural language processing to grasp what you mean, not just what you type.
● Summarize and Synthesize
Instead of handing you a pile of files, it gives you digestible answers or summaries, saving you hours of reading.
It’s not just about finding information; it’s about making it actionable. This is why tools like Copilot are indispensable—but it’s also where they become a double-edged sword.
Behind the magic of Microsoft Copilot lies a Retrieval-Augmented Generation (RAG) model, a sophisticated GenAI architecture that combines:
● Data Connection
Copilot is plugged into your organization’s ecosystem—emails, SharePoint, OneDrive, Teams, CRMs and more.
● Retrieval Layer
When you ask a question, Copilot identifies the most relevant data sources and fetches the required information. It has access to all of the infomration it’s connected to.
● Generative AI
It then processes this data, generating concise responses or summaries tailored to your query.
Think of it as a relay race: your question triggers a search across all connected systems, retrieves data based on relevance, and passes it to the model to craft the answer.
Here’s the twist: enterprise search is only as secure as the data it’s connected to. Most organizations, unfortunately, have some degree of data misconfiguration—think files with incorrect permissions, overly broad access, or outdated restrictions.
Now, combine this with RAG models like Microsoft Copilot. These tools don’t just search—they embed your data’s permissions and accessibility rules into the AI model itself. That means:
The result? Oversharing information becomes a real concern:
● False Sense of Security
Misconfigured permissions might allow sensitive data to slip through the cracks. Users can unintentionally craft queries that extract data the AI “knows” but shouldn’t reveal.
● Data Exfiltration
Employees with ill intentions—or even curiosity—can exploit the system by carefully phrasing prompts to access restricted data.
● “Just a Question Away”
Unlike traditional tools, where accessing sensitive information might involve several hoops, generative AI simplifies the process. A well-crafted prompt is often all it takes to expose confidential details.
These issues can result in significant risks, including sensitive data exposure, compliance violations, and data leakage.
So, how do you balance the incredible productivity gains of tools like Microsoft Copilot with the real risks of oversharing and regulatory exposure? It’s no secret that data misconfiguration is inevitable, and even the best data governance programs can take years to mature—if they ever fully do.
Still, you can’t afford to wait. Generative AI’s transformative power is too valuable to shelve indefinitely. The solution lies in assessing your initial risks and continuously monitoring Copilot’s usage to detect and respond to potential security issues.
Here’s how:
As highlighted earlier, oversharing risks aren’t just theoretical—they’re happening today. Remember the example where Copilot inadvertently exposed PCI data during a simple query? Many enterprises recognize this danger and hesitate to roll out Copilot organization-wide until they fully understand the implications.
To manage this, you need to evaluate your risk before deployment:
● Simulate Real-World Scenarios
Test what Copilot might retrieve when handling sensitive queries. For example, could it surface data related to PCI or customer payment disputes?
● Automate Your Benchmarking
Instead of manually assessing potential risks, leverage automation tools to evaluate your data configuration against best practices. This gives you a clear understanding of where sensitive data might be exposed and helps you determine what to address and prioritize before scaling usage.
Once Copilot is deployed, the key to safe usage is visibility, monitoring, and automated remediation:
● Gain Visibility
Identify all data sources connected to Copilot and track what’s being accessed during queries. Know whether sensitive data is inadvertently being retrieved.
● Monitor Prompt Activity
Analyze prompts and responses in near real time. Assess whether sensitive data is being consumed by unauthorized users and whether it poses a risk to compliance or privacy.
● Automate Remediation
Oversharing incidents often stem from outdated configurations or overly broad permissions. Instead of manual fixes, automate the process of updating permissions and re-embedding them into the model.
● Apply Your Organization’s Policy
Ensure Copilot aligns with your enterprise data security policies and compliance standards by enforcing data governance and access control policies. Automate enforcement to continuously mitigate risks in Copilot interactions.
To ensure your team can manage risks effectively without adding unnecessary overhead, integrate Copilot monitoring into your existing security ecosystem:
● Connect to Your SIEM
Route events like data misconfigurations or oversharing incidents directly into your security information and event management tools for centralized tracking.
● Leverage ITSM Workflows
Automate ticket creation and resolution for incident response teams, helping them address misconfigurations or permission issues efficiently with different stakeholders and data owners.
● Enhance with Automation Tools
Use workflow automation to remediate permissions, fix misconfigurations, connect with data owners to perform permission fixes, and ensure updates are applied seamlessly, reducing the burden on your security and IT teams.
Generative AI tools like Microsoft Copilot are transforming the way organizations work, delivering incredible productivity gains. But with great power comes great responsibility—particularly when it comes to safeguarding sensitive data and maintaining compliance.
By taking proactive steps, such as assessing your initial risk, implementing continuous monitoring, and seamlessly integrating remediation workflows with your existing security tools, you can harness the full potential of Copilot without fear of oversharing or regulatory pitfalls.
At Opsin, we specialize in helping organizations like yours roll out Microsoft Copilot securely from day one. Our solutions ensures your deployment is both powerful and protected from day one.
Ready to unlock the full potential of Copilot while staying secure? Schedule a demo with us today to see how Opsin can help you achieve it.
