Artificial Intelligence Governance
In Part I of this article, we will explore the challenges surrounding AI Governance. It's becoming increasingly clear that most new cybersecurity products involve some form of machine learning (ML) or artificial intelligence (AI). The growing interest in AI is evident, with many organizations already purchasing AI solutions or planning to do so—often without fully understanding the broader implications of adopting this technology. It's essential for organizations to recognize that AI must be governed through policies, procedures, and other key considerations like ethics, accountability, and transparency. Additionally, businesses must ensure that AI applications in areas such as Human Resources do not lead to biased or unjust outcomes.
Gartner defines AI as "advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions." Stanford's definition of ML is “the science of getting computers to act without being explicitly programmed.” In simpler terms, ML is a subset of AI that empowers machines to improve their performance through experience.
For the sake of clarity in this article, we'll refer to all systems using AI, ML, and algorithms collectively as “AI”.
Key Advantages of AI:
- Reduces human workloads
- Increases precision in task execution
- Processes large volumes of data quickly
- Improves quality of life in various sectors
- Enhances human cognitive abilities and decision-making
AI in Security and Compliance:
Here are some ways AI is being applied in the realm of security and compliance:
- Extracts actionable insights from data using advanced analytics
- Identifies potential failures and threats before they materialize
- Flags inefficient operational and maintenance workflows
- Automates repetitive tasks in security and compliance
- Augments human analysis, improving decision-making
In corporate governance, AI can offer organizations cutting-edge solutions for problem-solving, market predictions, and risk management—far surpassing traditional methods. A good starting point for companies is developing a strategic AI governance framework to outline clear guidelines on how AI should be used across the organization.
Key Challenges in AI Governance:
- Can AI systems be designed to align with human values, such as fairness, accountability, and transparency?
- How can we ensure the safety and certification of AI technology, so its use doesn't cause harm?
- What are the privacy implications when AI, powered by data, makes autonomous decisions?
- What will be the impact on jobs as AI is integrated into more aspects of work?
The Board of Directors should carefully consider the following questions to better understand the opportunities and risks associated with AI adoption. These considerations will also serve as a foundation for defining the organization's AI governance approach:
- Can AI comply with existing legal and regulatory requirements?
- Does AI align with the company's ethical standards?
- Have we evaluated how AI could transform our products/services, and which areas of the business could benefit from automation or ML?
- How might AI integrate with other emerging technologies we are already investing in?
- Do we have the necessary computing infrastructure to support AI?
- Are we equipped with the digital skills and talent to move forward with AI adoption?
- How will we build trust with stakeholders if we implement AI?
- Have we considered how to handle data collected by AI?
- Are we addressing cybersecurity risks and data privacy concerns?
Benefits of Well-Structured AI Governance Policies:
- Articulates ethical and legal principles to guide decisions regarding acceptable AI use
- Aligns decision-making across the organization with established ethical standards
- Enhances legal compliance
- Promotes transparency and information-sharing within the company
- Ensures consistency in decision-making and compliance efforts
Next week, in Part II, we will dive deeper into the specifics of AI policies. For this, we've drawn insights from articles and interviews published by PWC, Corporate Compliance, Gartner, and Priti Ved's work, “Leveraging Artificial Intelligence and Machine Learning for Security and Compliance.”
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