Governance

Increased employee productivity is the promise of AI, but measuring it remains elusive. This article explains why effectiveness is a better metric than productivity and how organizations can turn AI usage data into improving GenAI outcomes.

Although AI adoption is growing, productivity impact remains limited. While 14% of employees use GenAI regularly, only 0.1% currently demonstrate the skill and frequency needed to drive meaningful productivity gains. The data underscores the importance of measuring GenAI usage, skill development, and business outcomes.

Short answer: If your organisation falls under NIS2, every generative AI tool your employees use is part of your regulatory risk surface. The single most important thing to know is what AI is being used and on which plan — because a personal or free GenAI account handles your data very differently from a business plan, and that difference can turn everyday productivity into a compliance gap.

Generative AI is accelerating faster than governance, leaving many organizations struggling to balance innovation with security. Join Northamber and NROC Security for a webinar that shows how productivity and safe GenAI use can go hand in hand.

Generative AI is no longer experimental. It is already part of everyday work in most organisations. The growing problem is – the guardrails and training needed to make GenAI safe are not keeping up.

Our updated CISO guide explains how traditional security architectures are challenged by this new breed of business software and the best practices CISOs can implement. This guide enables CISOs to mitigate the risk of data leaks, prevent compliance violations, provide a great end user experience, and contribute to the overall business goal of driving personal productivity using GenAI.

Practical ways to build end user confidence, skills, security, and governance so employees can realize productivity gains from GenAI tools like ChatGPT.

Employee productivity is entering a new phase as GenAI tools move from experimentation to everyday work. We founded NROC Security on the belief that enterprise employees want to use tools like ChatGPT, to get more done, while organizations remain rightly concerned about security and data exposure. By the end of 2025, most organizations had stopped saying “no” to GenAI, and instead started introducing policies and staff guidance for acceptable use, even as the technical enforcement lagged behind.

AI initiatives are quite often governed company-wide, but identifying different characteristics in the initiative is essential to ensure focus to right execution and get improved results.

MSSPs are under pressure to deliver AI relevance as generative AI adoption is accelerating faster than traditional security controls can catch up. Enterprises are embracing tools like ChatGPT, Copilot, and Gemini across departments, often without oversight — creating fresh risks, compliance obligations, and uncertainty. For MSSPs, this represents both a challenge and an opportunity: a chance to provide clarity, guardrails, and governance that make GenAI security a top customer priority.

Your colleagues are using GenAI right now—but probably not in the way your IT team intended. From data leaks to app overload, organizations are learning that enabling GenAI isn’t just about buying a license—it’s about rethinking policy, trust, and productivity. Here’s what we’ve learned.

Before diving into the how of security and governance tooling, you must first understand the what of enforcement. Evaluating tools based solely on their technical features and implementation details won’t deliver meaningful results. Success in GenAI governance starts with clearly defining what needs to be enforced—only then can you determine how to do it effectively.

Best Practices for Securing Public GenAI Apps and LLM Apps in the Enterprise