Managing algorithmic risks presents new and challenging challenges for governance, risk, and compliance professionals as AI transforms how businesses operate. Cory Grace, an AI consultant for STI, and Chris Porter, CEO of Training Camp, discuss how to develop sound AI governance frameworks in a world where regulations are constantly evolving.
Every industry is experiencing a governance crisis as a result of the AI revolution. Only 35% of organizations have formal AI governance frameworks in place, despite the fact that 73% of organizations currently use AI in some capacity. This is a risky gap that exposes companies to operational, regulatory, and reputational issues.
“We’re seeing something that has never happened before,” says Chris Porter, CEO of Training Camp, who has assisted numerous businesses in addressing AI governance issues. ” AI systems that are constantly learning and developing are incompatible with traditional risk management techniques. It’s like attempting to use tools designed for stationary objects to control a moving target.
The Most Critical Issue for GRC Teams
Adding new risk categories to existing frameworks is only one aspect of the problem. The risks associated with AI differ greatly from those that are currently present. We need new methods for evaluating and managing these risks.
Working with multiple clients, STI AI consultant Cory Grace has direct experience with this issue. According to Grace, “Instead of totally altering their methods, companies always attempt to incorporate AI into their existing frameworks.” ” They’re overlooking hazards that conventional GRC frameworks weren’t designed to manage.
Among the risks associated with AI are algorithmic bias, which produces unfair results; model drift, which results in unpredictable behavior changes; black box decision-making, which fails to meet explainability standards; and complex data dependencies, which render traditional data governance inadequate.
Porter emphasizes that there are significant blind spots as a result of the vast knowledge gap between GRC specialists and AI technology. “GRC professionals need to understand what that means for various groups of people and situations when your data science team claims that their model is 95% accurate.” What you do not know, you cannot control.
The first step to effective AI governance is education. GRC specialists don’t have to be data scientists, but they do need to understand AI well enough to make informed decisions and pose intelligent queries.
“The biggest mistake people make is thinking that AI governance is just an extension of traditional IT governance,” according to Porter. ” Businesses believe they can handle AI risks in the same manner as they handle other technological risks, but AI systems operate very differently. They are capable of learning, changing, and acting in ways that static controls cannot foresee.
This educational gap is filled by Training Camp’s AI Foundations course, which equips businesspeople with the knowledge they need to distinguish between AI hype and reality and comprehend the laws governing AI.
“I’ve seen too many organizations try to govern AI without really understanding the technology they’re trying to govern,” Grace says, highlighting the significance of having this fundamental knowledge. This ignores actual risks while giving people a false sense of security.
Navigating the ever-changing rules and regulations
The rapidly evolving laws and regulations surrounding AI have both positive and negative implications for GRC practitioners. The EU AI Act, revisions to the NIST AI Risk Management Framework, and sector-specific regulatory guidance are all establishing new compliance requirements. Businesses must move swiftly to satisfy these demands.
“The pressure from regulators is growing quickly,” Grace says. “Groups cannot afford to put off learning governance skills until there is complete regulatory certainty.”
Porter concurs and emphasizes the necessity of adaptable approaches: Building adaptive governance systems now that can expand with changes in the law is the wise course of action. “Organizations that wait for regulatory certainty will always be behind the curve.”
This entails setting up frameworks that are risk-based, proportionate to the impact of AI systems, auditable with thorough documentation, adaptable to new capabilities, and compatible with ongoing business procedures.
Implementing the Framework
Prominent businesses are creating comprehensive AI governance systems rather than merely adhering to the regulations. Porter identifies several key elements shared by all successful implementations:
“Organizations need frameworks like MITRE ATLAS and OWASP Top 10 for LLMs to systematically identify AI-specific threats,” according to Porter. “These are practical tools for evaluating risk in the real world; they are not merely theoretical exercises.”
Including the lifecycle: Astute businesses incorporate controls into every stage of the AI development process, from data collection to model deployment and monitoring, rather than considering AI governance as an afterthought.
Porter asserts that “annual risk assessments are not enough for AI systems.” “They must always be watched.” “To identify bias, performance declines, and compliance drift, you must monitor things in real time.” Because AI systems can alter their behavior in response to new information, your governance strategy must also be adaptable.
These governance framework implementations are used in real-world scenarios for Training Camp students enrolled in the AI Advanced course. They perform tasks like audit preparation, explainability evaluation, and threat modeling.
Proactive AI Governance’s Competitive Advantage
While both experts agree that when implemented strategically, AI governance can give a company a significant competitive advantage, many businesses view it as a compliance burden.
According to Porter, “companies with mature AI governance frameworks aren’t just reducing risks; they’re also making it easier for people to adopt AI more quickly and confidently.” “By reducing regulatory risk, improving AI outcomes by identifying and correcting bias, building stakeholder trust through transparent practices, and streamlining operations through process integration, strong AI governance promotes faster innovation.”
This competitive advantage has been observed by Grace in the businesses of her clients: “Companies that get AI governance right early can move faster and with more confidence than those that are still figuring out the basics.” Instead of impeding innovation, it turns into a tool that supports it.
A Strategy for Methodical Execution
According to experts, GRC professionals should adhere to a structured three-phase plan in order to develop their AI governance skills:
Phase 1: Building the Foundation entails ensuring that all members of the GRC team are proficient in using AI, compiling a comprehensive list of all AI systems, and examining the existing frameworks to identify any gaps unique to AI.
Phase 2: Framework adaptation, focuses on modifying current risk management frameworks to address risks unique to AI, developing focused AI governance guidelines, and ensuring that everyone is aware of their roles and responsibilities with regard to supervision.
Phase 3: Operational Integration establishes systems for ongoing monitoring, establishes regular cycles for evaluation and improvement, and implements governance controls for the entire AI lifecycle.
“The most important thing to keep in mind is that AI governance isn’t a one-time project; it’s a skill that needs to grow as your AI matures,” advises Porter.
The Need for Intervention
Both experts emphasize how crucial it is to take immediate action as the use of AI expands and regulations become more clear. There is less time to develop critical skills as regulations and competition become more stringent.
“We’re at a turning point,” Porter states. ” Governance, risk, and compliance (GRC) professionals who acquire AI skills now will be crucial strategic collaborators in their companies’ AI revolution. Those who don’t will find themselves increasingly marginalized as AI becomes more and more significant to business.
“The question isn’t whether AI governance will become necessary, but whether GRC professionals will be ready to lead when their companies need them most,” says Grace, who shares this sense of urgency. Now is the time to begin honing these abilities.
Both experts advise GRC professionals who are feeling overburdened by the scope of this task to begin by becoming more knowledgeable about AI, comprehending the particular risks, and modifying frameworks as necessary. Those who take immediate action will have a significant advantage over those who wait for someone else to resolve the issue.
