What the request is really asking for
Y Combinator’s Spring 2025 Request for Startups, “A Secure AI App Store,” was not simply a call for another catalogue of chatbots. The underlying opportunity is infrastructure: a trusted way for people and companies to discover, install, evaluate, permission, monitor, update, and remove AI applications.
The opportunity remains relevant in 2026 because AI products increasingly act through tools, APIs, business software, and internal data. A useful app store for AI must therefore answer questions that ordinary software marketplaces often avoid:
- What can this application access?
- Which model, tools, and third-party services does it use?
- Can its actions be reviewed or reversed?
- How are prompts, documents, and customer data handled?
- What happens when the model changes or the application is compromised?
For Indian founders, this is a chance to build products for local workflows—finance, healthcare, education, logistics, legal services, and public-sector operations—without treating security as a final compliance layer.
Why AI distribution is harder than software distribution
Traditional applications usually have predictable permissions and deterministic functions. AI applications can interpret untrusted instructions, call tools, generate code, retrieve sensitive records, and take actions with varying levels of reliability. An app-store model must make those risks visible before installation and contain them during use.
The strongest products will likely combine four layers:
- Identity and provenance: Verify who publishes an application, what code and models it contains, and whether dependencies are known.
- Permissioning: Give agents narrowly scoped access to data, tools, spending limits, and business systems.
- Evaluation: Test accuracy, refusal behaviour, prompt-injection resistance, privacy leakage, latency, and cost before and after release.
- Operations: Provide logs, alerts, version controls, rollback, incident response, and transparent update policies.
A marketplace alone is not the product. The defensible layer may be the runtime, security gateway, evaluation network, enterprise administration console, or trust data generated through usage.
Product directions founders can test
Founders should avoid building a broad directory before validating a narrow, high-value use case. Potential wedges include:
1. A verified marketplace for enterprise agents
Create a catalogue where every listing includes permissions, supported integrations, data-retention rules, evaluation results, pricing, and publisher identity. Start with one buyer segment—such as Indian mid-market companies using Microsoft 365, Google Workspace, Salesforce, or common accounting systems.
2. A security gateway for AI applications
Route agent requests through a control plane that enforces policy, redacts sensitive fields, blocks dangerous tool calls, and records an auditable trail. This is especially relevant where an AI system can initiate payments, edit customer records, or send external communications.
Teams building complex agent systems should pair this with practical controls described in How to Secure Autonomous AI Workflows.
3. An evaluation and certification layer
Offer repeatable tests for prompt injection, data exfiltration, hallucination, privilege escalation, harmful outputs, and tool misuse. Certification should be evidence-based and time-bound; a model or dependency update can invalidate old results.
4. A private app store for regulated organisations
A hospital, bank, university, or government department may need a private catalogue rather than a public marketplace. The product could support internal publishing, approval workflows, tenant isolation, self-hosted connectors, and local data controls. A local-first architecture can be useful where sensitive workloads must remain within an organisation’s environment; see Secure Local-First Operating Systems for Privacy.
5. A distribution and billing platform for AI builders
Many small AI developers struggle with authentication, metering, subscriptions, support, and enterprise procurement. A platform that handles these operational tasks—while exposing usage and security information—could become the “app store” layer without owning every application.
Minimum viable product for an Indian startup
A credible first release does not need thousands of listings. It needs a constrained environment in which security claims can be tested. A practical MVP could include:
- A developer submission flow with identity verification and dependency disclosure.
- A standard application manifest covering tools, scopes, models, data stores, regions, and retention.
- A sandbox that limits network access, filesystem access, secrets, and tool permissions.
- Automated tests for common prompt-injection and unsafe-action scenarios.
- Human review for high-risk categories.
- An admin console for approvals, logs, budgets, version pinning, and revocation.
- A clear incident process and security contact for every published application.
Build the first workflow around a measurable customer pain point. For example, a support agent that drafts replies is easier to evaluate than a general-purpose agent authorised to modify orders, issue refunds, and contact vendors.
Security and compliance considerations in India
Security design should reflect the data and sector in which the application operates. Map every data flow, including model providers, observability tools, vector databases, contractors, and backup systems. Obtain explicit customer approval before sending sensitive information to an external model, and give administrators controls over retention and deletion.
Founders should also prepare for Indian privacy obligations, contractual security reviews, and sector-specific requirements. Do not claim “secure” because an application uses encryption alone. Document threat models, access controls, testing scope, known limitations, subprocessors, and breach-response procedures.
For agentic systems, apply least privilege by default. Separate read and write permissions, require confirmation for irreversible actions, use spending and rate limits, and make tool calls inspectable. If the product automates business operations, AI Workflow Automation for High-Growth Startups offers a useful lens for selecting workflows that can be automated without hiding accountability.
How to make the YC application stronger
The original Spring 2025 framing should not be treated as a promise that a specific 2026 programme or deadline is open. Check YC’s official application page for current dates and requirements. Regardless of timing, an application should demonstrate:
- A sharp customer: Name the buyer, user, and first workflow rather than saying “every AI user.”
- A painful problem: Show failed audits, unsafe actions, deployment delays, or procurement friction.
- A working prototype: Demonstrate installation, permissioning, execution, monitoring, and revocation.
- Evidence of demand: Include pilots, design partners, usage, retention, or paid commitments.
- A technical insight: Explain why your approach is safer or more scalable than wrapping an existing model API.
- A distribution advantage: Show how you will reach developers, enterprise teams, or a focused vertical.
Use concrete failure cases. A short demonstration of an agent being blocked from exporting customer data is more persuasive than a slide claiming “enterprise-grade trust.”
A practical 90-day build plan
Days 1–30: Interview security, IT, and operations buyers. Select one workflow, define its threat model, and write the application manifest and permission model.
Days 31–60: Build the sandbox, audit trail, approval flow, and evaluation suite. Run adversarial tests against your own product and document failures rather than hiding them.
Days 61–90: Deploy with two or three design partners. Measure blocked unsafe actions, successful task completion, false positives, latency, cost per task, and time saved in security review. Convert those results into a focused case study.
The winning product in this category will not be the marketplace with the most listings. It will be the trust and control layer that makes AI applications safe enough to install, useful enough to retain, and transparent enough for organisations to approve.