What does Prima Partners do?

Prima Partners is the advisory practice of Prasanna Walimbe. It operates in three modes: AI & IT strategy advisory for CXOs, corporate AI enablement and delivery, and angel investment plus mentoring for early-stage founders in fintech, supply chain and applied AI.

Who is Prasanna Walimbe?

Prasanna Walimbe is a deep-tech operator with 20+ years of experience across AI evaluation and safety, AI/ML architecture, and financial research. He is an alumnus of IIT Kharagpur and IIM Calcutta and a Certified Independent Director advising boards on cross-border AI investments, operations and compliance.

Which sectors does Prima Partners focus on?

Fintech and BFSI, supply chain, manufacturing, and applied AI across enterprise use cases. Assurance work is mapped to the EU AI Act, ISO 42001 and the NIST AI Risk Management Framework.

How do I engage Prima Partners?

Email pw@prima-partners.com to start a conversation. Engagements run globally on a remote-first basis from Dubai.

What is AI governance consulting and what frameworks do you use?

AI governance consulting maps an organisation's AI systems, decisions and controls to a recognised risk framework — typically the EU AI Act, ISO 42001 and the NIST AI Risk Management Framework — and closes the gaps. Prima Partners runs checklist-based evaluations, model-risk reviews and board-level assurance for financial services, supply chain and applied-AI use cases.

Do you serve boards as an Independent Director?

Yes. Prasanna Walimbe is a Certified Independent Director and advises boards on AI strategy, cross-border investment and operations, and compliance obligations across jurisdictions — with a specialisation in fintech, supply chain and applied AI.

Do you offer corporate AI training and enablement?

Yes. Corporate AI enablement includes executive workshops, leadership AI literacy, hands-on programmes for delivery teams, and practical playbooks — AI realism vs hype, ML deployment, GTM for AI products, and the founder's mindset inside corporates.

Do you mentor or invest in startups?

Prima Partners writes angel cheques and provides hands-on mentoring to pre-seed and seed founders in fintech, supply chain and applied AI. Support covers technical architecture, GTM, commercial modelling and board readiness.

Which AI model should an enterprise standardise on?

The Stanford 2026 AI Index shows the performance gap between top models has collapsed, so standardising is now a switching-cost and security decision rather than a capability one. Reframe long-running evaluations around integration risk, data-egress controls and exit paths instead of another round of benchmarks.

Should companies slow junior hiring because of AI?

The Index's data on entry-level task exposure argues for redesigning the junior role, not deleting it. Cutting the pipeline solves an immediate headcount problem but removes the bench that becomes your senior engineers and managers by 2028.

Where do AI agents actually belong on the enterprise roadmap?

The Index's agent-benchmark results locate a narrow band of workflows where agentic systems clear the reliability bar today — mostly bounded, tool-using tasks with human review. Broader deployments across open-ended enterprise workflows are a 2027 conversation and should be scoped as such.

Can enterprises trust reported AI productivity gains?

The Index places self-reported productivity uplift directly beside realised-output figures and the two diverge sharply. Boards should measure cycle time, defect rate and unit economics on specific workflows rather than treat satisfaction survey scores as evidence of P&L impact.

Who should sign off on enterprise AI governance?

The Index's transparency scores show foundation-model vendors are disclosing less each year, so control cannot be inherited from the vendor stack. Governance sign-off belongs with an internal owner running a checklist mapped to the EU AI Act, ISO 42001 and the NIST AI Risk Management Framework.

Why rank controls by customer effort rather than by architecture?

Because the architecture diagram does not tell a sponsor what to fund first. Effort is driven by the state of the existing estate, not by the vendor's product.

Can we start with the quick wins alone?

Yes — a gateway, DLP and audit lineage typically get an initiative past its first security review within weeks. They are necessary but never sufficient: they do not enforce who may do what.

What does a Phase 0 assessment deliver?

In two to four weeks: a data-estate and IAM-maturity map, an entitlement-gap analysis, a threat model, a build-vs-buy recommendation, and a prioritized roadmap sequenced by effort and consequence.

Securing enterprise AI: an effort–impact prioritization framework

A reference architecture answers what controls a governed AI agent needs. This framework answers the question a customer actually decides on: in what order, and is it worth it?

How to read the two axes

Customer-side effort measures what the customer must do, not what the consultant builds — low means configure a bought component, high means re-plumbing systems or discovering information that does not yet exist. Enterprise consequence measures severity if the control is absent, weighted by irreversibility, breadth of exposure and regulatory liability.

Master mapping

Foundations: policy and entitlement model, identity propagation (on-behalf-of), data classification. Core: database read enforcement (RLS and masking), permission-aware RAG retrieval, action controls with human-in-the-loop and limits, non-human identity governance. Quick wins: LLM firewall or gateway, PII redaction and output DLP, observability and audit lineage. Opportunistic: trained refusal disposition — never an enforcement boundary.

Consequence anchors

Impact ratings are anchored to IBM Cost of a Data Breach 2025 (global average $4.44M; US $10.22M). Missing action controls produce irreversible loss at machine speed — a telecom SIM-swap failure produced a $33M arbitration award. Missing read enforcement produces mass PII/PHI disclosure; healthcare has been the highest-cost sector for 14+ years at about $7.42M.

Sequenced roadmap

Phase 0 assessment (2–4 weeks, low customer effort). Phase 1 quick wins plus foundation kickoff (4–8 weeks). Phase 2 enforcement on one lighthouse use case (6–12 weeks). Phase 3 scale and govern, ending with the customer owning the policy model.

Hidden effort drivers

Identity propagation through shared service accounts and connection pools; undocumented entitlement discovery; classification debt; RAG ACL drift; legacy coarse authorization; and unclear ownership of the policy model across security, data, platform and application teams.

Frequently asked

Why rank controls by customer effort rather than by architecture?: Because the architecture diagram does not tell a sponsor what to fund first. Effort is driven by the state of the existing estate, not by the vendor's product.
Can we start with the quick wins alone?: Yes — a gateway, DLP and audit lineage typically get an initiative past its first security review within weeks. They are necessary but never sufficient: they do not enforce who may do what.
What does a Phase 0 assessment deliver?: In two to four weeks: a data-estate and IAM-maturity map, an entitlement-gap analysis, a threat model, a build-vs-buy recommendation, and a prioritized roadmap sequenced by effort and consequence.

pw@prima-partners.com · LinkedIn