2023 was that year AI has become mainstream. Several years into the boom, AI tools are already deeply integrated into the way we work. 9 out of 10 companies report that employees regularly use personal AI tools. From simple tasks like composing an email to powering agent systems that autonomously perform multi-step tasks, AI has become a hidden productivity engine behind the scenes in most organizations.
But there’s no such thing as a free lunch. Its use increases security gaps. The use of personal AI in the workplace has become almost universal; Only 4 out of 10 companies actually have a formal LLM subscription. Shadow AI (the use of unauthorized AI tools) enforces a familiar tension heard frequently (sometimes weekly) from security leaders: either block AI and lose productivity, or allow AI freely and accept the risk.
Neither extreme is ideal. There are hidden risks to unauthorized AI use, but blocking it altogether can deprive your business of valuable productivity gains.
So how do we actually resolve this paradox, especially at scale?
Enabling AI – Putting proper guardrails in place
There it is. Since AI is already a part of operations, the real challenge is giving employees the space to use it without opening the door to data breaches (not to mention compliance gaps). Here are four key areas to curb usage:
visibility
It all starts here. If you want to manage AI risk, you need a clean inventory of what’s in use across your environment. This means you can scroll through the live list of AI applications and quickly find:
- Which apps are being used?
- Which users are accessing it?
- Where is it being accessed from?
- Security and compliance attributes that each app has.
This is where many teams are first surprised. A long trail of unknown or authorized apps. Seeing which of these applications are gaining traction can also help you better assess risk and prioritize the right gaps.
analysis
Once you know what’s at play, the next step is to understand the risks that surface in context. Not all AI deployments are the same. Some models may be running in an approved environment, while others may have been generated somewhere they shouldn’t be, such as on a personal device.
The analysis should give you an answer like this:
- Is the app enterprise ready?
- Are you meeting compliance requirements?
- What is my organization’s readiness for this tool?
Context is the difference between awareness and informed risk management.
real-time monitoring
Organizations need the ability to inspect activity within AI tools like ChatGPT in real time. This includes monitoring prompts, uploads, and responses to detect when sensitive information may be compromised.
For example, opening prompts flow normally, but prompts containing sensitive data are flagged and blocked before they leave the enterprise. That means it will never reach ChatGPT. bingo.
classification
Some co-pilots and AI assistants use in-house data during inference, but if you don’t categorize that information properly, you run the risk of giving the AI information it shouldn’t have access to at the direction of your employees.
By classifying and applying labels to sensitive data through integrations such as Microsoft Purview Information Protection, organizations can consistently identify and protect data. Teams can also prevent data from being used in AI inference, avoid accidental exposure through AI chat prompts, and sanitize data before using it to train models.
Although often overlooked, this step is perhaps the most important, especially as companies expand their use of AI.
control
Finally, organizations need the ability to enforce policies. Of course, this does not mean blocking with blunt force. Effective teams actually rely on detailed controls such as:
- Allows prompts, but prevents file uploads.
- Permanently block high-risk applications.
- Restrict use of personal accounts.
- Prevent sensitive data from leaving your environment.
Control is what enables safe AI deployment and Sustainable. Organizations will be able to apply consistent rules to protect data, and employees will be able to use AI tools to increase productivity. Everyone wins.
AI and data protection are not necessarily mutually exclusive
of Symantec CloudSOC console It brings all these capabilities together into one unified workflow: discovery, analysis, monitoring, classification, and control. With built-in support for two of the most popular enterprise AI assistants (Microsoft Copilot and Google Gemini), organizations deploying Symantec DLP Cloud can gain real-time visibility, inspection, and enforcement across the AI tools their employees actually use.
What’s the result? The ultimate goal for all security and business leaders is to ensure that sensitive data remains secure throughout its lifecycle, while employees remain productive and innovative.
Watch these features in action in my on-demand webinar. Preventing the proliferation of AI applications.
