Managing AI risks: Policies, privacy and practical safeguards your organization needs to know
In McDonald Hopkins’ April webinar, “Managing AI Risk: Policies, Privacy, and Practical Safeguards,” attorney Karen Bridges discussed how organizations can adopt AI while managing legal, operational, and reputational risk. As AI use accelerates, governance is lagging, leaving companies exposed to evolving regulations and the application of legacy laws to new technologies.
The core message: organizations need a practical AI governance framework that aligns use cases with risk, implements clear controls, and evolves alongside regulatory change.
Governance is lagging behind adoption
Many organizations are deploying AI tools without fully understanding how they work, what data they use, or how outputs are generated. At a minimum, companies should be able to identify what each system does, what data it relies on, who it impacts, and how it is monitored. Without this baseline, risks range from regulatory scrutiny to data exposure and reputational harm.
Everyday tools, elevated risk
AI transcription and meeting tools present immediate exposure. Recording and processing sensitive conversations through third parties can compromise privilege, trigger compliance obligations, and create data transfer risks.
To mitigate this, organizations should classify meetings by sensitivity, limit or disable recording in high-risk contexts, require consent, and negotiate clear vendor terms around data use, retention, and security. Even default features like auto-sharing transcripts can unintentionally expose confidential information.
Building a practical framework
Effective governance starts with disciplined intake: define the business purpose, assess risk, and map data flows. AI use cases should be tiered by impact, with stricter controls applied to high-stakes decisions in areas like HR, finance, and healthcare.
Organizations should also establish clear rules around data usage, model training, and vendor accountability, supported by strong contractual protections.
Accountability, oversight, and control
Companies must be prepared to explain how their AI systems function, including data inputs, testing processes, and safeguards against bias. Human oversight remains critical, particularly for high-impact decisions.
Key controls include:
- Role-based employee training
- Maintaining an internal AI inventory
- Monitoring for performance issues and anomalies
- Approval workflows and defined shutdown mechanisms
Vendor and data risk
Because many AI tools rely on third parties, vendor risk management is essential. Organizations should prioritize privacy-protective configurations, validate security standards, define IP rights, and establish clear incident response protocols.
Shadow AI and misuse
Unauthorized use of public AI tools, or shadow AI, is a growing concern. Employees may input sensitive data into unapproved platforms, creating compliance and security risks.
Organizations should monitor usage, restrict high-risk tools, provide approved alternatives, and train employees on acceptable use.
Accuracy and trust
AI outputs can be persuasive but inaccurate. To maintain quality and credibility, organizations should treat outputs as drafts, require human review, and enforce verification standards. Responsibility for accuracy ultimately remains with the user.
Emerging threats: deepfakes and impersonation
AI-driven impersonation is increasing, enabling fraud through realistic voice and media manipulation. Safeguards such as out-of-band verification, authentication protocols, and incident response planning are critical.
Conclusion
AI can drive meaningful value, but only when managed with the same discipline as cybersecurity and data privacy. Organizations that implement practical governance, maintain transparency, and keep humans in the loop will be better positioned to innovate while minimizing risk. If you have questions about your organization’s AI use or policy, contact attorney Karen Bridges.
