Organizations adopting AI need to answer three different questions: How do we identify and reduce AI risks? How do we make AI governance consistent across the business? And which legal requirements apply to our AI systems? The NIST AI Risk Management Framework (AI RMF), ISO 42001, and the EU AI Act address these questions differently. The NIST AI RMF provides voluntary risk-management guidance, ISO/IEC 42001 specifies...
Read More
Artificial intelligence is becoming part of everyday business operations. Organisations are using AI to automate processes, analyse customer information, support financial decisions, improve cybersecurity, and increase productivity. However, as AI adoption increases, so do the risks associated with these technologies. An AI system may produce inaccurate results, expose sensitive information, introduce unfair outcomes, or make decisions that are difficult to explain. These risks can affect customers, employees,...
Read More
Governance, risk, and compliance (GRC) professionals face a challenging environment as organisations adopt artificial intelligence, manage growing cybersecurity threats, and respond to changing regulatory requirements. Traditional compliance approaches are becoming harder to maintain. Organisations must manage multiple regulatory frameworks, demonstrate that their controls are working, monitor third-party risks, and provide leadership with reliable information about their risk exposure. Attending the right GRC conferences in 2027 can help...
Read More
The NIST AI RMF Generative AI Profile contains more than 200 suggested actions for managing generative AI risks. These cover everything from governance and data privacy to testing, security, and incident response. Working through every action without considering its relevance to your AI systems can waste time and resources. Some actions will address risks your organization faces directly, while others may be less relevant to your...
Read More
Generative AI can help businesses create content, analyze information, write code, and automate routine tasks. But it also introduces risks that traditional software controls may not fully address. An AI assistant might invent legal citations, expose confidential information, or generate misleading content that employees mistake for fact. The NIST AI RMF Generative AI Profile helps identify and manage these risks. Published as NIST AI 600-1 in...
Read More
Artificial intelligence is increasingly influencing decisions that affect customers, employees, patients, and entire communities. Financial institutions use AI to evaluate credit applications, healthcare providers deploy algorithms to support clinical decisions, and businesses integrate generative AI into customer service, recruitment, and operational workflows. These applications create opportunities for greater efficiency and innovation, but they also introduce consequences that organizations must understand before deploying AI systems. An AI system...
Read More
Artificial intelligence is becoming part of everyday business operations, influencing how organizations assess risks, serve customers, manage employees, and make important decisions. As AI adoption expands, businesses face growing expectations to demonstrate that their systems operate responsibly and that potential consequences have been properly evaluated. An AI system may deliver accurate technical results while still creating unintended consequences for individuals, communities, or organizations. Automated recruitment tools...
Read More
COBIT and COSO both help organizations improve governance, manage risk, and strengthen controls. However, they address different needs. COBIT focuses on governing and managing enterprise information and technology, while the COSO internal control framework provides a structure for designing and evaluating controls across operations, reporting, and compliance. Organizations do not necessarily have to choose one over the other. They can use COSO to establish internal control...
Read More
An internal control system does not operate in one department or support only one type of objective. Controls can apply to financial reporting, operations, and compliance across different levels of an organization. The COSO cube illustrates this relationship. It brings together three dimensions of internal controls: objectives, components, and organizational structure. The model shows how the five components of internal control apply across different objectives and...
Read More
An effective internal control system needs more than individual policies or control activities. Organizations also need the right oversight, risk assessment, information, communication, and monitoring processes to make those controls work. The COSO internal control framework organizes these requirements into five components and 17 principles. The 2013 framework uses these principles to make the requirements for effective internal control more explicit and to provide a structure...
Read More