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💻📱🖥️ AI Systems Are Advancing Faster Than Risk Controls, Warns Info-Tech Research Group in New Risk Management Resource

Saturday, 12 July 2025 04:48.PM

- As AI adoption accelerates across industries, most organizations remain unprepared to manage the complex and emerging risks that come with it. Info-Tech Research Group, a global research and advisory firm, has recently published a resource that introduces a proactive, principle-based framework to help enterprises formalize their AI risk programs, improve governance, and align strategies with business objectives. -

Organizations are adopting AI at a rapid pace, but many lack the necessary controls to manage the risks that come with these transformative systems. From hallucinations and bias to deepfakes and adversarial threats, AI can introduce novel vulnerabilities that traditional governance frameworks were not designed to address. To help organizations tackle these challenges, global research and advisory firm Info-Tech Research Group has recently published its research insights in the blueprint, Build Your AI Risk Management Roadmap, offering a structured methodology to develop a comprehensive, business-aligned AI risk program.

As outlined by Info-Tech's AI risk management framework, failing to manage AI risk proactively can lead to regulatory violations, reputational damage, and lost value. Despite these consequences, many organizations still rely on ad hoc processes, react to issues only after they occur, or silo risk ownership within technical teams without business involvement.

"AI risk is a business risk. Every AI risk has business implications," says Bill Wong, research fellow at Info-Tech Research Group. "Accountability cannot rest with AI leaders alone. Business executives must be active participants in identifying, evaluating, and responding to AI risks, and that starts with embedding risk management into governance, strategy, and decision-making processes."

The firm's resource outlines how to evolve fragmented or informal approaches into a structured AI risk management program through four key dimensions: risk governance, risk identification, risk measurement, and risk response. One of the blueprint's themes focuses on aligning the AI risk framework with broader enterprise risk management to ensure the program integrates with organizational strategy and regulatory requirements.

To support implementation, Info-Tech has introduced a comprehensive roadmap built around framing AI risks, establishing AI risk governance, identifying and assessing risks, measuring potential impact, defining responses, and creating a roadmap for execution. A key component of the blueprint is the formation of an AI Risk Council (AIRC), which would include cross-functional representation from IT, AI, and business leaders. This council is responsible for assigning ownership, recommending risk tolerance, reviewing risk assessments, and ensuring shared accountability across the organization.

Info-Tech's framework also emphasizes the need to establish foundational AI principles, such as explainability and transparency, fairness, data privacy, safety and security, validity and reliability, and accountability. These principles, derived from global frameworks such as those developed by the Organization for Economic Co-operation and Development (OECD), serve as the ethical and operational backbone of responsible AI.

Key Processes to Operationalize AI Risk Management

Info-Tech's resource is designed to help organizations reduce the number of unidentified risks, build realistic contingency plans, enable cross-functional accountability, and improve regulatory compliance, such as with the EU AI Act's high-risk system requirements. It also supports better decision-making and ongoing monitoring to ensure AI systems remain aligned with organizational goals.

The firm's Build Your AI Risk Management Roadmap blueprint outlines the following steps for IT leaders to operationalize AI risk management across the entire organization:

1. Establish Foundational AI Principles – Define the ethical and operational standards that guide the development and deployment of AI.

2. Assess AI Risk Management Maturity – Understand the current state of AI risk governance to identify capability gaps.

3. Create and Assign AI Risk Council Responsibilities – Establish clear accountability for AI risk across leadership and governance teams.

4. Implement an AI Risk Management Framework – Develop an AI risk management program that begins with introducing an AI risk governance program that is aligned to the organization's foundational AI principles Then determine methods for identifying and classifying AI risks, followed by establishing how AI risks will be measured and monitored, and finally adopting a strategy for the actions to take to mitigate a given AI risk.

5. Pursue AI Risk-Mitigation Initiatives – Prioritize actions that reduce the likelihood or impact of AI risks based on feasibility and value.

6. Build an AI Risk Management Roadmap – Translate priorities into a structured, time-bound action plan aligned with business goals.

The blueprint promotes a preventative mindset, encouraging organizations to detect, assess, and mitigate AI risks before they materialize, transforming risk management from a reactive obligation into a strategic enabler.

SOURCE: Info-Tech Research Group

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