In 2025, the balance between risk and reward became materially more consequential. Advances in AI, rising expectations for operational resilience, and intensifying regulatory scrutiny reshaped executive agendas and exposed the limits of reactive risk management. Some organizations adapted quickly, using governance, risk, compliance, ethics, and learning to move faster with confidence. Others struggled to keep pace.
Third-party risk rarely announces itself with alarms. More often, it arrives quietly, disguised as an assumption. The assumption is that responsibility can be shared without consequence. That accountability can be distributed, diluted, and still hold its shape when pressure arrives. That contracts, frameworks, and carefully worded clauses will stand in for human judgment when systems fail and decisions cannot wait.
In this article, Ayoub Fandi examines how organizations can unlock untapped value in their existing GRC platforms by applying an engineering mindset rather than defaulting to new tools or costly overhauls. Drawing on practical experience, he explores why most GRC platforms remain significantly underused and how data optimization, strategic integrations, and workflow design can transform them from passive documentation systems into active drivers of risk and control execution.
For years, cybersecurity has been treated like a home alarm system. You install it, arm it, and hope it only goes off when something truly bad happens. The problem is that modern cyber threats no longer behave like burglars rattling windows at night. They act more like termites, quietly weakening structures over time, or like flash floods that overwhelm defenses faster than alarms can react. In this environment, reacting after the fact is no longer enough. Organizations must move from reactive cybersecurity to proactive cyber resilience.
In my latest post, I discuss how if you look at how enterprise risk management is practiced today, you’d be forgiven for thinking that the entity-level risk register sits at the center of ISO 31000 and COSO ERM. It doesn’t.
Managing risks across the AI/ML lifecycle is critical for building reliable, secure, and ethical models. From data collection and labeling to training, fine-tuning, and evaluation, each stage presents unique challenges that can affect performance, reproducibility, fairness, and safety. Implementing well-defined controls ensures models are trustworthy, auditable, and resilient to both technical and operational issues.
There’s a certain kind of growth story you see all the time in digital media. Big launches. Loud claims. Paid distribution quietly doing most of the work behind the scenes. This isn’t that story. What started as a small, independent experiment has, in a remarkably short period of time, turned into something far more consequential: a place where governance, risk, and compliance professionals actually come back, not because they’re chased by algorithms, but because the content respects their time and intelligence.