About Data Engineering for AI
RIT helps research teams and partners identify, manage, and document the risks that come with AI enabled tools. We support responsible AI adoption by aligning projects with security, privacy, compliance, transparency, and human oversight expectations from the beginning of the technical lifecycle.
Our team helps find how an AI tool should be reviewed, monitored, documented, and governed, so it can be used safely in high-accountability research, clinical, administrative, and public sector environments.
Responsible AI governance integrates security, transparency, monitoring, and human review thourghout the solution lifecycle.
How RIT Help Govern AI Safely
Align AI projects with security and compliance standards
RIT partner with IT security, risk and compliance groups across the University, and with external parties as needed, to identify the policies, regulations and standards that apply to your work. We develop specifications and architectures to ensure compliance. We have experience with HIPAA, human subjects research under the Common Rule, and requirements grounded and NIST 800–53 and 800–171, and with emerging framework such as the NIST AI RMF and state of Ohio IT–17 AI use requirements. We embrace FAIR standards for data management and governance.
Build human oversight into the workflow
RIT codesigns AI workflows to carefully, delineate where human or AI agents can and should perform decision-making, review and verification, including exception and escalation pathways.
Create transparent and inspectable AI outputs
RIT supports AI tools that allow users to understand and verify results. This includes source, attribution, clear disclaimers, model or data documentation, audit trails, prompt, and response logs, and review points that help user assess whether an AI output is grounded and appropriate.
How RIT Help Govern AI Safely
Audit readiness and accountability
RIT supports documentation practices that make AI projects traceable and reviewable overtime. This may include audit logs, model cards, data cards, version-controlled records, source documentation, and defined accountability for human-in the-loop-review.
Governance of AI assets
RIT helps teams document and manage AI related assets, including source data, prompts, embeddings, modern configurations, knowledge bases, generated outputs, and user feedback. We treat these assets as governed materials that require ownership, access controls, versioning, and lifecycle management.
Security and privacy risks
RIT collaborate with with security, privacy, risk, and compliance team to design systems and minimize or mitigate those risks. Rebuild systems on top of infrastructures designed to be both compliant and effective.