From Cowboy to Compliant: The Startup Opportunity in Enterprise AI Governance - Our latest Insights at Born & Kepler

In our latest episode of Born & Kepler, we sit down with Jeffrey Paine from Golden Gate Ventures to explore what really holds enterprise AI back from scaling.

Walk into almost any enterprise today and you’ll find AI proof-of-concepts tucked into teams — a customer-support pilot here, a contract assistant there, a sales enablement demo in the corner. Yet the leap from pilots to production is stalling.

It’s not that the models aren’t powerful enough. The real bottleneck is governance: consistent outputs, explainability, security, compliance, and deployment realities like on-prem or VPC setups. As Jeffrey puts it, “We’re still a bit in the cowboy phase, but that’s changing fast.”

And for startups, this compliance and governance layer is becoming the overlooked market where real, durable revenue will be built — the infrastructure that will turn AI from impressive demos into dependable systems.

🎧 Listen to “Building Success in Southeast Asia: A Chat with Jeffrey Paine"

This is not a minor niche. Budgets for AI increasingly sit with IT, security, and risk as much as with lines of business. Regulatory momentum is real, from the EU AI Act to sector-specific guidance in finance and healthcare. While the modeling layer is noisy and fast-moving, the control plane above it—the policies, tests, approvals, and audits—is more stable, more integrated with existing enterprise processes, and arguably more defensible. Startups that help organizations make AI safe, compliant, and explainable will unlock stalled adoption and capture value that is not tied to any one model provider.

Think of the enterprise AI governance stack as a set of interconnected layers: evaluation and testing, policy and risk management, data governance, deployment architecture, monitoring and incident response, and regulatory compliance automation. Each layer can be a standalone wedge to win initial customers, and together they form a platform that turns AI experiments into production systems. Here is how those layers break down and where startups can enter with a clear, narrow promise that enterprises will buy.

Evaluation and testing. Enterprises need to know whether a given model and prompt solve a specific task to a defined standard. Generic benchmarks are not enough. Teams need test harnesses that capture task-specific metrics: hallucination rate thresholds, sensitivity to prompt variations, bias and harm checks, and reproducibility across versions. A winning wedge here is a test system that integrates directly into CI/CD for ML and prompts, auto-generates golden datasets from real workflows, and produces evidence packs security teams can read without becoming prompt engineers themselves. Make it easy to compare vendors, track changes, and sign off with confidence.

Policy and risk management. Enterprises have policies for everything from data retention to acceptable use. AI needs the same treatment. Startups can map use cases to risk tiers, define required controls per tier, and encode those controls as guardrails that sit between users and models. Think: enforced redaction of sensitive fields, forbidden data sources, approval workflows for new prompts or connectors, and automatic documentation that satisfies internal auditors. The key is bridging security and product teams: policy should be expressed in business language, compiled into technical enforcement, and measured continuously rather than annually during audits.

Data governance. The fastest way to get blocked by a CISO is to be loose with data. A practical product angle is a data privacy layer tailored to LLMs and multimodal models: detect and redact PII and secrets before they reach a model, enforce column-level access on retrieval-augmented generation, and maintain complete audit trails of what data was used when and by whom. Add region-aware routing to respect data residency, and you have a concrete value proposition most enterprises understand in minutes. When you integrate with existing DLP, key management, and access control systems, you lower switching costs and speed up approvals.

Deployment architecture. For many large organizations, “SaaS only” is a non-starter for sensitive workloads. A strong differentiator is shipping with opinionated, well-documented deployment options across VPC, on-prem, and even air-gapped environments. Provide Helm charts, Terraform modules, and reference architectures for Kubernetes and OpenShift. Support GPU scheduling and budget controls out of the box. Offer a model catalog with pre-approved options and a path to add new ones through a governed intake process. In regulated industries, these details are not nice-to-haves—they are the deciding factor between a pilot and a contract.

Monitoring and incident response. AI systems drift, prompts get tampered with, and new failure modes appear in production. Startups can offer real-time monitoring for quality and safety regressions, prompt injection detection, data exfiltration defenses, and automatic rollback to known-good configurations. Pair that with human-in-the-loop escalation, clear runbooks for incidents, and integrations with SIEMs like Splunk or security operations tools. The measurable promise is faster time to detect and resolve issues with fewer false positives—language that resonates with security leaders and operations teams alike.

Compliance automation. The EU AI Act and similar frameworks introduce explicit obligations: risk management, data governance, technical documentation, record-keeping, and transparency. A pragmatic product is a compliance workbench that ties together the evidence from your testing, policy, data, and monitoring layers into living documentation. Translate legal obligations into technical checks, provide gap analysis per use case, and generate audit-ready packages. This is workflow software, not a legal opinion. The win comes from reducing the time and uncertainty between a team wanting to ship and a risk function authorizing it across countries with different expectations and cadences.

Selling this stack requires a realistic view of demand. In any given country or company, there are roughly three customer classes. First, those with budget who are actively searching for solutions; they will move quickly if you meet their requirements. Second, those with budget but low understanding; they need education and clear proof of value. Third, champions who love the idea but have no approved budget; they need a path to secure funding. Map where your buyer sits—by city, sector, and company maturity—and calibrate expectations accordingly. That is how you avoid burning cycles on enthusiastic conversations that cannot close this quarter.

Wedge strategies work best when they solve a painful, well-bounded problem. Examples: a red-teaming and prompt safety service that finds exploitable failures in customer-facing chatbots; a PII and secrets firewall that sits in front of all model calls; an explainability pack for contract review that shows exactly what evidence was retrieved and how the output was composed. Each wedge gets you into the account and gives you usage data, relationships with security and legal, and credibility to expand into adjacent layers over time. The wrong move is to sell a vague, horizontal “AI platform” before you have earned trust on a specific job to be done.

Integration is not a feature; it is the product. Ship day-one support for SSO and SCIM provisioning, logging to the customer’s SIEM, DLP hooks, key management systems, ticketing tools, and procurement portals. Provide a security questionnaire library with pre-filled, transparent answers. Publish a short, plain-language overview that a non-technical risk reviewer can understand. The more you speak the language of the enterprise with artifacts they already use, the faster the path from a technical champion to a signed order form. Many great products die in procurement because they underestimate this layer of work.

On-prem readiness deserves special mention. Some enterprises, and many in regulated sectors, simply cannot send sensitive data to third-party clouds. That does not mean they cannot adopt AI. It means the vendor must package its software for their environment, including resource controls, observability, hardware acceleration, and patching processes that match how IT operates today. If you can make the on-prem path as frictionless as a cloud trial—down to reference GPU configurations and cost calculators—you unlock buyers your competitors will not even attempt to serve. This is especially relevant in regions where data sovereignty expectations are stringent and cloud trust is uneven across countries.

Pricing and packaging benefit from clarity. Many buyers will anchor governance spend against risk reduction and time-to-production, not pure usage. Consider pricing based on protected users, governed use cases, or compliance scope rather than tokens. Offer a starter tier that converts pilots to paid usage quickly, then land-and-expand into broader policy management and monitoring. Where possible, tie pricing to measurable outcomes: number of AI workflows moved into production, audit readiness time reduced, or security findings closed. Selling governance as insurance resonates, but pairing it with productivity gains is how you avoid being cut when budgets tighten.

Regional playbooks matter. Adoption curves, culture, and budgets vary by city and sector. Some countries have deep in-house build capacity; others prefer to buy. In Southeast Asia, adoption often trails the U.S. by 9 to 12 months, which can be an advantage: you can watch what works, localize for language and regulation, and arrive with a more complete product. Data residency rules differ, procurement can take longer, and stakeholder maps change across markets. Build for these realities rather than assuming a one-size-fits-all motion. The founders who win do the unglamorous work of tailoring deployment, documentation, and pricing country by country.

Fast iteration still wins. Even in governance-heavy domains, speed matters because ideas are converging and competition is fierce. Strong product leadership is less negotiable than ever. The teams that will break through ship quickly with design partners, learn from real production constraints, and keep tightening the loop between what risk teams require and what users actually need to get work done. Lean teams can now reach meaningful ARR with modest capital if they land in buyer environments ready to purchase from startups. That combination—capital efficiency plus product velocity—is a real edge in the current cycle.

Measure what buyers care about. Useful north-star metrics include: time from pilot start to security approval, percentage of AI use cases that move from POC to production, mean time to detect and resolve AI-related incidents, audit readiness time for a new use case, false positive rates in safety controls, and unit economics across deployment models. You can still track the usual SaaS metrics, but surfacing these governance-specific outcomes in your product and sales narrative signals that you are solving the buyer’s real problem, not just selling technology they have to babysit after purchase.

A 90-day plan to first paid pilots might look like this. Week 1–2: choose a specific wedge and define three measurable outcomes security and legal will accept. Week 3–4: recruit three design partners across two sectors; secure a champion and a risk reviewer at each. Week 5–6: build a minimal test harness, policy engine for one control, and audit log with export; publish your security overview and deployment guide. Week 7–8: ship integrations for SSO, SIEM logging, and one DLP. Week 9–10: run red teams and capture evidence packs; iterate prompts, controls, and runbooks. Week 11–12: convert at least one pilot to a paid starter tier tied to a production use case. Keep scope narrow, outcomes explicit, and approval friction low.

Investors evaluating this space should be mathematical about timing and scale. The right company at the wrong adoption curve can take 14 years to mature, which breaks fund math. Look for regulatory forcing functions with clear deadlines, buyer personas that control budget today, and expansion paths from a wedge into adjacent layers of the governance stack. Diligence should stress-test entry valuations, the capital required over multiple rounds, and whether a lean team can reach revenue thresholds in your target geography. Founders who can sell to CISOs and CIOs—while still solving for line-of-business productivity—have a structural advantage. The best will demonstrate capital efficiency, not just promise it.

Risks exist. Regulations will evolve, model providers will change interfaces and capabilities, and some pieces of the stack will commoditize. The mitigation is to anchor on workflow and integration depth rather than thin wrappers. Own the data paths, the policy definitions tied to real obligations, and the bridges into existing enterprise systems. Be explicit about your stance on open-source versus proprietary models so customers know you will not lock them in. Avoid selling only horizontal control planes too early; earn the right to platform status by solving painful jobs at depth and expanding thoughtfully from there. In governance, trust is cumulative and revocable. Act accordingly.

Step back, and the thesis is simple. The largest driver of enterprise AI value will not be a single breakthrough model; it will be the ability to put AI to work safely and repeatedly in production. That requires making AI systems explainable, governable, secure, and deployable within real-world constraints. The companies that take AI from demos to dependable will sit on the critical path of budget, earning durable, high-margin revenue. They may not be flashy, but they will be the ones that compound as AI moves from novelty to infrastructure across industries and regions.

Takeaway: the shortest route from AI hype to enterprise revenue runs through the compliance and governance layer. If you are a founder, pick a narrow wedge with clear evidence of demand, integrate deeply with security and IT from day one, and ship faster than the conversation moves. If you are an investor, back teams that understand regional buyer realities, can sell to risk functions, and stay mathematical about adoption curves and fund size. The cowboy phase is ending. The builders who make AI boring—in the best sense—will own the next wave. 🎧 Listen to “Building Success in Southeast Asia: A Chat with Jeffrey Paine"