Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The Monetary Authority of Singapore (MAS) on AI Risk Governance

‍ ‍The Monetary Authority of Singapore's (MAS) Consultation Paper on Guidelines on Artificial Intelligence Risk Management, released in November 2025, dramatically altered how AI is positioned within the country’s financial supervision. The document states that the proposed Guidelines "set out MAS' supervisory expectations relating to AI risk management in financial institutions (FIs)" (p.3).

How Organizations Should Prioritize AI Security Risks

‍ ‍Artificial intelligence (AI) systems and GenAI tools are no longer merely being experimented with in the market. Instead, they are being embedded into the organizational infrastructure at large, shaping how enterprises process data, automate decisions, and provide core services to customers. Unfortunately, while this integration increases efficiency, it simultaneously increases exposure to a dramatic extent.

Ensuring Institutional AI Ownership With the AI Compliance Officer

‍Artificial intelligence (AI) systems and generative AI (GenAI) tools have already been embedded across enterprise operations in a myriad of ways that trigger compliance obligations, both in terms of AI-specific regulations and other reporting mandates. In many cases, this adoption is occurring informally, through employee-driven tools or AI features embedded within third-party platforms, without centralized visibility or approval.

Quantified Cyber Risk Through an ERM Lens in NIST IR 8286 Rev. 1

Lack of data has rarely been a challenge that cybersecurity leaders in the enterprise setting have faced. In fact, cyber risk data is usually in abundance. The obstacle, thus, is instead twofold. Teams must first make sense of all of that information, and leadership must then be able to communicate what it means in a language that supports high-level decision-making. That gap between information and deeper understanding is where many cyber risk programs flounder.

6 Cyber Risk Quantification (CRQ) Trends That Will Define 2026

‍Cyber risk quantification (CRQ), the process of modeling cyber threats and forecasting loss outcomes, is becoming foundational to how organizations govern and respond to cyber exposure. What began as a specialized function is now shaping the priorities of security operations and enterprise risk management as a whole.

AI Risk Governance Suite - office hours part 1

Kovrr’s new AI Risk Governance Suite gives enterprises the visibility, structure, and measurable control needed to manage GenAI responsibly across its full lifecycle. Join us for Office Hours: Part 1, where Or Amir will walk through the first three modules of the suite—showing how enterprises can gain real-time oversight and quantifiable insight into their AI landscape: Discover how these capabilities help enterprises align innovation with accountability—building a defensible foundation for responsible GenAI adoption.

Finding the Best AI Governance Software for Enterprises

‍ ‍AI governance software provides GRC leaders and security and risk managers (SRMs) with a dependable way to understand how AI is being used across the business and whether safeguards are functioning as intended. The software can translate a complex ecosystem of tools and models into concrete insights that stakeholders can evaluate.

Transforming AI Risk Awareness Into Measurable AI Governance

Only a few years ago, after more than a decade of debate over how cybersecurity incidents affect the financial stability of public companies, the U.S. Securities and Exchange Commission (SEC) finally made cyber risk disclosure a formal requirement. The intent was to bring transparency and accountability to a category of risk that had long been treated as technical rather than financial. Now, albeit voluntarily, AI has entered that same conversation, but the speed of its arrival has been remarkable.

Communicating AI Risk to the Board: Bridging the AI Governance Gap

‍AI is altering business operations and workflows at a pace that few leaders have experienced before. GenAI deployments are rising across every department, expanding their influence and maximizing business productivity and efficiency. However, the moment the conversation shifts from AI's advantages to its inherent risk, the dynamic changes.

19 AI Risk Leaders Driving Enterprise Transformation

‍ AI has moved from experimentation to everyday infrastructure, shaping decisions and workflows across nearly every industry. However, in the rush to harness its speed and efficiency, many enterprises adopted GenAI and other AI systems faster than they built the structures necessary to govern them. The result is an all-too-familiar pattern of powerful technology being deployed widely before its risks are fully understood, let alone managed. ‍