IEEE Boston Reliability / IEEE Boston AI Local Group / WIE / SMTA presentation: From AI Experimentation to Enterprise Governance: Six Practitioner Frameworks for Operationalizing AI Oversight
This event will be hybrid, but we encourage in-person attendance. Registration is free, but is required so that we can plan pizza, salad, and beverages.
When arriving Wednesday evening, please be prepared to show a government-issued photo ID, such as a drivers license, at the main gate at Wood Street. State that you are attending the IEEE Reliability meeting in the Main Cafeteria.
After parking, walk towards the main building near the flagpole. Before entering, look left, and walk down the steps. At the bottom of the steps, turn right. Walk straight through the double set of double doors and straight into the Main Cafeteria.
Looking forward to seeing you in person!
Dan Weidman
Chair, IEEE Boston Reliability Chapter
Date and Time
Location
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- MIT Lincoln Laboratory
- 244 Wood St
- Lexington, Massachusetts
- United States 02421
- Building: Main Cafeteria
- Click here for Map
- Starts 06 September 2026 05:00 AM UTC
- Ends 12 October 2026 05:00 PM UTC
- No Admission Charge
Speakers
Mohan Namasivayam
From AI Experimentation to Enterprise Governance: Six Practitioner Frameworks for Operationalising AI Oversight
From AI Experimentation to Enterprise Governance: Six Practitioner Frameworks for Operationalizing AI Oversight
Most organizations have moved AI (artificial intelligence) from pilot to production faster than their governance has moved from policy to practice. The result is a familiar gap. Board-level principles and regulatory frameworks such as the NIST (National Institute of Standards and Technology) AI Risk Management Framework (RMF) and the EU (European Union) AI Act exist, but the delivery team has no instrument that turns either into a decision it can make on an ordinary working day. This talk closes that gap with six practitioner frameworks, each built for a specific governance decision:
- which features need which level of oversight,
- how much autonomy an AI agent may be given and on what evidence,
- what "done" means for a system whose output varies from run to run,
- how to measure the cost of a wrong autonomous action before expanding its authority,
- how to version and roll back agent behaviour, and
- how to trace a live agent decision back to the requirement that permitted it.
Each framework is mapped to the relevant NIST AI RMF function and EU AI Act obligation, so the audience leaves with tools that satisfy the auditor and the engineer at the same time. The session closes with a 30-day implementation roadmap that a single team can execute without waiting for an enterprise program.
Biography:
Biography:
Mohanaraman (Mohan) Namasivayam is an IEEE Senior Member and product owner for an enterprise investment data platform used by global institutional asset managers, with 17 years in financial data systems. Mohan writes on requirements engineering and governance for autonomous AI, with work published by Modern Analyst, BA Times, and IIBA, and manuscripts under review at IEEE IT Professional and IEEE Software. He serves on the Program Committee of IEEE BigData 2026 and is an MBA candidate at Boston University Questrom School of Business.
Address:Massachusetts, United States
Agenda
5:00 pm doors open, for networking. Arriving earlier is ok.
5:30 pm: Pizza, salad, and beverages arrive, while networking continues.
6:00 pm: Introduction to the presentation, followed by the formal presentation.
About 7:30 pm: Formal presentation and formal Q&A end.
About 8:00 pm: Informal Q&A and networking end.