The Risk Posed by Opposition to Artificial Intelligence
In today's digital world, AI is becoming an integral part of numerous industries, from oil drilling to lemonade stands. However, the question on everyone's mind is: how can we ensure our employees leverage AI safely without compromising company data, privacy, or security?
The use of AI often involves sensitive information, such as customer data, pricing, and financial data. Recognizing where AI sits in the business is crucial to understand its impact on revenue and productivity, and to provide protection while enabling its use.
A proactive approach is essential. Proactive communication, such as sending newsletters or running webinars, is crucial to ensure employees know how to use AI safely within the organization. Building trust over time between security and employees is also key for successful partnerships and informed AI adoption with practical guardrails.
Unfortunately, 38% of employees share sensitive work information with AI tools without their employer's permission. Using a prohibitive approach to AI use can drive people to use it in unsafe and unmonitored ways, creating security gaps. To prevent this, clear AI governance policies that balance business needs with security requirements are recommended.
The best practices for developing these policies involve a structured, multi-step approach. First, define clear objectives and governance scope, mapping AI use cases aligned with business goals while assessing readiness regarding data, processes, and stakeholders.
Establish comprehensive AI policies that address ethical use, fairness, privacy, transparency, and security, referencing global principles such as the OECD AI Principles for guidance on fairness, accountability, explainability, and safety. Assign well-defined roles and responsibilities, such as an AI governance council, AI product owners, ethics/compliance officers, and operational committees for ongoing policy enforcement and review.
Catalog and inventory AI assets and data pipelines with metadata capturing model details, training data, objectives, performance metrics, and data quality controls to ensure data readiness and governance compliance. Engage cross-functional teams early to understand current AI use, potential risks, and opportunities, ensuring that policies cover compliance, security, and ethical implications comprehensively.
Provide actionable and practical guidance for employees, encouraging safe AI experimentation with approved tools that incorporate built-in security and privacy guardrails to reduce risk and foster innovation. Incorporate ongoing monitoring and policy updates to keep pace with evolving AI capabilities, regulatory changes, and emerging risks to maintain relevance and effectiveness over time.
Prioritize transparency and explainability in AI systems to build trust and support regulatory compliance, while ensuring accountability through clear ownership and governance mechanisms. Address security and safety explicitly, protecting AI systems from vulnerabilities to ensure reliable operations that meet both business continuity and security standards.
Leverage advanced AI tools, such as Agentic AI, to automate compliance processes and analytics, improving efficiency and robustness in governance frameworks. Together, these steps create a balanced AI governance framework that supports business innovation while rigorously managing security and ethical risks in line with recognized standards and evolving regulations.
As AI continues to move from 'experiment' to 'essential,' with enterprise spending jumping 130%, it's clear that a well-thought-out AI strategy is no longer a luxury, but a necessity. Security teams that say "no" to everything can drive people to use technology underground, leading to a loss of oversight. Instead, let's aim for informed adoption with effective guardrails that work in practice. Let's strive for a future where AI is a tool that enhances our work, not a source of worry.
[1] Deloitte. (2020). The AI governance imperative. Retrieved from https://www2.deloitte.com/content/dam/Deloitte/us/Documents/about-deloitte/us-consulting/us-ai-governance-imperative.pdf
[2] McKinsey & Company. (2020). The new normal in AI governance. Retrieved from https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-new-normal-in-ai-governance
[3] World Economic Forum. (2020). Guidelines for AI ethics and governance. Retrieved from https://www.weforum.org/agenda/2020/05/ethics-and-governance-guidelines-for-ai/
[4] Forrester. (2021). The future of AI governance: A Forrester Consulting Thought Leadership Paper Commissioned by Google Cloud. Retrieved from https://www.forrester.com/content/the-future-of-ai-governance-a-forrester-consulting-thought-leadership-paper-commissioned-by-google-cloud
Artificial Intelligence (AI) is not only prevalent in various industries, but it's also increasingly being used within organizations, raising questions about safety, data privacy, and security. To mitigate potential risks, a proactive and balanced AI governance approach is paramount, involving clear policies, regular communication, and the use of advanced AI tools that prioritize transparency, explainability, and security.
Informed adoption of AI, equipped with effective guardrails, can transform it from a source of worry into a valuable tool that enhances work performance, all while adhering to recognized standards and evolving regulations.
