As AI becomes a staple of day-to-day work, conversations about governance and safety are growing louder. But while many companies are racing to publish AI policies or roll out guidelines, far fewer can answer a basic question:
Is any of it actually working?
AI Risk reduction sounds good in theory. But in practice, it’s notoriously hard to quantify—especially when the AI risks are emerging, contextual, and often invisible until it’s too late.
That’s why forward-thinking organizations are shifting focus from just setting policy to measuring impact.
The Problem with Traditional Metrics
Most enterprise risk dashboards aren’t built for generative AI. They focus on:
- Endpoint protection
- Access controls
- Network vulnerabilities
But none of these capture the real-time decisions employees make when using tools like ChatGPT, Copilot, or Bard.
Adding fuel to the fire is the fact that traditional benchmarks often fail in the dynamic environments where AI operates today. And the fact that outdated metrics usually fail to capture the true impact of these systems in real-world applications.
According to an article from AI Impact Weekly, in order to capture a higher ROI from AI investments, companies will need to pivot to measuring AI success based on long-term business outcomes. The same could then be said for AI risks. You can have airtight infrastructure—and still leak sensitive data if someone pastes confidential text into a chatbot.
What AI Risk Actually Looks Like
To measure risk reduction, you first have to understand where AI introduces it. Common patterns include:
- Sensitive data exposure: Confidential info shared with third-party models
- Undisclosed AI-generated content: Text used in legal, PR, or academic contexts without attribution
- Tool misuse: Use of unapproved or unvetted AI platforms
- Policy fatigue or avoidance: Employees ignoring guidance or using AI “off the record”
These aren’t hypothetical—they’re daily behaviors. And they require new kinds of metrics.
What to Track Instead
Here are meaningful indicators of whether your AI governance efforts are reducing risk:
- Prompt risk alerts: Frequency and resolution of flagged risky inputs (e.g., pasting customer data into chatbots)
- Policy compliance rate: % of AI use happening through approved tools, with attribution/disclosure where needed
- Behavior change over time: Are nudges reducing high-risk actions? Are users pausing before submitting prompts?
- Shadow AI detection: Volume of unsanctioned tools in use, and change after awareness/training
- Audit trail completeness: Can you trace how AI-generated content was produced and used?
These aren’t just vanity metrics—they reveal how well your interventions are actually working.
For this, one could also look into advances in academic thinking and industry standards. Giudici et. al. (2024) proposed a Risk Management System consisting of four main principles: Sustainability, Accuracy, Fairness, Explainability. In the case of Amazon Web Services, they also wrote a blog post on the use of ISO/IEC 42001:2023 for AI governance.
Risk Reduction ≠ Zero Risk
Let’s be clear: you’ll never eliminate all AI risk. But that’s not the goal. The goal is to make the risks visible, reduce their likelihood, and create a feedback loop where behavior improves over time.
Like with phishing or insider threats, the presence of a few flagged incidents isn’t necessarily failure—it’s a sign the system is working.
How Tripwire Helps
Tripwire is built to measure risk where it happens—in the browser, at the moment employees are using AI tools. It captures anonymized insights into:
- The types of risky behavior being prevented
- Which policies are being followed vs ignored
- How user habits are evolving over time
This gives compliance, IT, and legal teams a new level of visibility—not just into what’s going wrong, but what’s improving.
You can’t manage what you don’t measure.
If your company is serious about safe, responsible AI use, start tracking the signals that actually matter.
Want to see what that looks like in practice?
- Learn more about our solution through the post: Introducing Tripwire
- Apply for early access and get our exclusive whitepaper
- Or just follow along as we explore how to make AI use at work safer, smarter, and more human