Because AI is so strategic, I engage with CISOs, CIOs, and data privacy officers who are optimizing their AI strategies. These are their stories.
The potential of generative AI to transform business is immense. I regularly meet with enterprise security, IT, data protection teams who are working to deploy and manage AI safely and cost-effectively. Because AI is so strategic, I also engage with CISOs, CIOs, and data privacy officers who are optimizing their AI strategies.
The Dramatic Rise in Enterprise AI Use
Singulr customers are experiencing a surge in AI adoption. Here are numbers that highlight both surging use and risk:
- 500+ unique AI services in use, adding 30 new services per week over the last three months.
- 75% of employees using unvetted AI services (Shadow AI), leading to security risks and uncontrolled costs.
- 80+ SaaS applications with embedded AI, many defaulting to “AI on” without governance.
Read the Singulr blog: The Enterprise AI Surge is Real and happening right now.
Knowledge is Power: Turning Discovery into Action
Discovering AI adoption patterns—services, users, and contextual information—is just the first step. The real value comes from acting on this data to reduce risk and cost while accelerating safe AI adoption. Here’s how five different organizations are using Singulr to achieve these goals.
1. AI Onboarding Team Drive Users to a safe Enterprise Account
Situation: A company had an enterprise Grammarly account licensed for 400 employees. Singulr found 800 employees using the tool, meaning 400 were on free-tier or personal accounts.
Issue: The enterprise account had safeguards against data collection for model training, but free-tier and personal accounts did not. A broad whitelist policy allowed Grammarly usage without distinguishing safe enterprise accounts from risky personal accounts.
Resolution: Singulr implemented fine-grained policies that blocked personal account use while directing users to join the secure enterprise account.
Result: Reduced data leakage risks and consolidated Grammarly usage, potentially lowering costs through volume-based pricing.
2. Development Manager Discovered Six Unapproved Development Co-Pilots
Situation: A development leadership team approved GitLab Duo as the AI coding assistant of choice based on security, functionality, and cost.
Issue: Developers were unaware of the decision and continued using self-selected AI tools. Unvetted development co-pilots posed risks such as proprietary code leaks and quality issues.
Resolution: Singulr’s Trust Feed™ identified non-standard tools in use. Policies were implemented to notify developers about GitLab Duo and discourage alternative tool usage. Over time, non-approved tools were blocked.
Result: Increased use of the approved AI tool while reducing security risks and costs associated with Shadow AI.
3. CIO Needed Usage Data to Measure AI Investment ROI
Situation: A CIO needed to validate claims from an AI SaaS provider that justified its high pricing.
Issue: Measuring efficiency gains from AI adoption was challenging without concrete usage data.
Resolution: Singulr provided detailed usage analytics, showing how frequently employees engaged with the AI service. Additional user feedback helped measure productivity gains.
Result: The CIO used data-driven insights to assess AI subscription ROI and optimize AI spending.
4. SVP Accelerated Risk Assessment to Clear AI Onboarding Backlog
Situation: The AI onboarding team faced a backlog of 1,500 AI service requests. The board of directors pressured them to speed up AI adoption while maintaining compliance.
Issue: The slow, manual onboarding process involved legal reviews for every request, blocking AI-driven innovation.
Resolution: Singulr’s AI Trust Feed™ automated risk research, providing compliance teams with pre-vetted information on AI tools, LLMs, and regulatory data.
Result: Faster onboarding, actively clearing the onboarding backlog, and the ability to enforce safe AI use policies while ensuring compliance.
5. IT Team Streamlined Vetting of Rapidly Changing AI Solutions
Situation: IT and security teams vetted new AI solutions but had no visibility into ongoing updates in homegrown AI applications embedded in customer-facing products.
Issue: Changes in data sources, AI models, and configurations introduced new risks that went undetected post-initial approval.
Resolution: Singulr provided real-time visibility into AI application changes, allowing IT teams to re-vet them efficiently.
Result: Streamlined AI governance with automated change management, maintaining security and compliance over time.
The Power of Singulr: Balancing Operational Efficiency and Safety
These success stories demonstrate the power of Singulr in helping enterprises operationalize AI governance, reduce risks, control costs, and accelerate adoption. As AI continues to reshape business, companies must take a strategic approach to managing its growth.
With Singulr, enterprises can stay ahead of AI sprawl while maximizing innovation and efficiency.
Additional Resources
To better understand the challenges of balancing operational efficiecy with safety>
Read the blog by our CEO and Founder Shiv Agarwal - Why I Founded Singulr AI
