AI governance,
with an engineer at the table.
I’m Jayanth Siddamsetty, an AI Governance Manager at Raisin, working in German banking. I help teams turn regulatory requirements, standards and organisational priorities into practical AI governance. My background in software engineering and applied AI research helps me understand the systems, shape technical decisions and assess the evidence.
How I work
Close to the technology.Useful to the organisation.
I approach AI governance with an auditor’s questions and an engineer’s understanding. As a governance manager, I bring those perspectives together to guide decisions, establish responsibilities and ensure follow-through.
Three perspectives informing better decisions.
How should the requirements shape what we build?
Understand how the system actually works: its data, models, integrations and likely failure modes. That shows where a requirement touches the design and what it will take to meet it.
Build the programme.Make it part of the work.
Leadership needs to know where AI is used, which obligations apply, who owns the risks and when to intervene. I connect that organisational view with the technical decisions, controls and evidence that make a governance programme work.
Apply the EU AI Act and ISO/IEC 42001.
I interpret the EU AI Act in the context of an organisation’s AI uses, including its role, risk classification and relevant obligations. I use ISO/IEC 42001 to structure the scope, policies and processes of an AI management system. Working with legal, risk, product and engineering teams, I turn these requirements into practical governance decisions.
Make the three lines of defence work for AI.
Shape how AI decisions are made and overseen. Keep risk ownership with the business, oversight and challenge with risk and compliance, and independent assurance with internal audit. Make clear who approves a new AI use case and when unresolved concerns require leadership action.
Carry requirements into development.
Those decisions need to shape the system itself. I work with technical and product teams to translate requirements into design choices, controls and testing while development is still taking shape.
Use evidence to review and improve.
Review the records, assessments and test results a team has already produced against applicable requirements. Check whether they support the decision, make gaps and limitations explicit, and use the findings to improve the system and its governance.
The technology isfamiliar territory.
My background combines software engineering in banking, data science at Fraunhofer and applied AI research at DFKI, where I built and trained deep neural networks. That experience shapes the questions I ask and how I work with the people building AI systems.
From engineeringto AI governance.
Evaluating the trustworthiness of AI systems was my entry into AI governance. Through MISSION KI’s Innovation and Quality Centre at DFKI in Kaiserslautern, I contributed to robustness testing and helped startups translate regulations and standards, including the EU AI Act, into technical requirements. That work connected technical evaluation with the decisions and responsibilities around AI use. Today, I bring that perspective to AI governance in German banking.
Contributions and training
I am a PECB certified ISO/IEC 42001 Lead Implementer and have completed ForHumanity’s Foundations of Independent Audit of AI Systems course.
I participated as an academic stakeholder in the drafting process of the GPAI Code of Practice and contributed to IAAA’s CAIA initiative.
Let’stalk.
Questions and ideas about AI governance are welcome.