AI investing has moved past hype cycles and into operational reality. Capital is no longer flowing only toward foundation models and headline-grabbing demos. It is moving toward infrastructure that quietly determines whether enterprises can operate safely in a world defined by distributed work, SaaS sprawl, and generative AI.

Yet many AI investments still fail for a familiar reason. The thesis sounds compelling, but the market need is not durable enough to survive scrutiny. The real question for investors is not whether a technology is innovative, but whether it solves a problem that enterprises cannot postpone.

Achal Singi, Vice President at WestBridge Capital, operates at that intersection of conviction and discipline. As an author of the top-selling book Care Navigation for the Growing Geriatric Population in the Age of AI, Singi approaches AI not as a trend, but as infrastructure that must prove economic necessity.

We spoke with him about what decision-grade investing looks like in the era of enterprise AI and why data protection has become one of the most defensible bets in the market.

Hi, welcome Achal. AI investing has become crowded. From your perspective, what separates durable opportunities from noise?

Hi, thanks for having me. The separation usually comes down to inevitability. If a company’s product is solving a problem that enterprises can delay for two or three budget cycles, it is not durable. If it is solving a problem that boards and CISOs lose sleep over, it becomes non discretionary.

After COVID, remote work dramatically increased the number of applications employees used and expanded the attack surface of enterprises. At the same time, generative AI introduced non human identities and new data leakage vectors. Sensitive information began flowing across Slack, Google Workspace, Microsoft 365, Jira, Salesforce, browsers, and now AI tools.

That convergence created inevitability. Enterprises could not simply rely on legacy perimeter security models. Data loss prevention had to evolve. That is where I built conviction.

You led a 32 million dollar investment into Nightfall AI in 2022, acquiring more than 20 percent ownership. What convinced you the opportunity was decision-grade rather than thematic?

Conviction required immersion. I conducted a deep dive into the Data Loss Prevention space and met multiple founders to understand their vision. I studied how enterprises were adapting to remote collaboration and where existing DLP vendors were failing.

Three signals stood out. First, the shift to remote and hybrid work was permanent. Second, SaaS proliferation created fragmented visibility into sensitive data flows. Third, AI adoption would accelerate the creation and movement of proprietary information.

Nightfall differentiated itself through modern architecture and customer love. Their platform did not simply scan static storage. It monitored data across communication platforms and cloud applications in real time. That aligned with how work was actually happening.

The investment was not based on momentum. It was based on structural change in how enterprises handle data.

Capital is one thing. Strategic partnership is another. What did your involvement look like post investment?

Writing a check is only the beginning. I became deeply involved in shaping the product roadmap and go to market evolution.

I spoke directly with 15 key customer accounts within my network to gather feedback and create a product wishlist. We mapped those requests against the existing platform and prioritized the most strategic additions. Through research and feasibility analysis, we narrowed the roadmap to three high impact launches: Data Exfiltration Prevention, Data Detection and Response, and Data Discovery and Classification.

These offerings expanded the platform beyond identifying sensitive data exposure to actively stopping data exfiltration, revoking inappropriate sharing, and preventing leakage into shadow AI environments.

I also conducted competitive research across seven major players including Cyberhaven, Code42, Netskope, Cloudlock, Forcepoint, Proofpoint, and Endpoint Protector. By analyzing their gaps and speaking with technical architects, we identified opportunities where Nightfall could differentiate and win market share.

Those roadmap decisions translated into cross sell opportunities, stronger retention, and measurable revenue growth.

Cybersecurity markets are crowded. What made this approach defensible at scale?

Defensibility comes from integration and automation. Nightfall expanded from Slack to Google, Microsoft, Jira, Confluence, Salesforce, Notion, Zendesk, web browsers, and AI applications. That breadth matters because data rarely lives in one place.

At scale, the platform has scanned over two billion items, detected more than one million exposed passwords and credentials, and achieved roughly 80 percent automated remediation for customers. That level of automation is critical. Enterprises cannot manually triage millions of events.

Large customers across technology, healthcare, financial services, and retail rely on the platform to protect their intellectual property and customer data. For many of them, the cost of a breach would reach hundreds of millions of dollars in damages. In that context, cybersecurity spend becomes protection of enterprise survival, not discretionary tooling.

Industry observers have described Nightfall’s offering as the first complete Data Leak Prevention solution for generative AI environments. How important is category creation versus execution?

Category language helps, but execution sustains it. Generative AI introduced new data leakage pathways that traditional DLP tools were not built to monitor. Positioning the company as a GenAI focused data leak prevention platform was important.

However, credibility came from real deployments. Customers like Snyk reported 94 percent true positives through automated workflows. Others automated sensitive data exposure policies and saved thousands of engineering and HR hours. Those outcomes make the category real.

In investing, you cannot rely on narrative alone. You need proof that the product changes behavior and budgets.

You also serve as a peer reviewer at SARC Journals and have written about AI’s role in healthcare navigation. How does that broader lens shape your investment decisions?

Peer review reinforces discipline. In academic evaluation, claims must be supported by evidence and clearly defined assumptions. I apply the same rigor to investment theses.

In my book on care navigation for the geriatric population, I explored how AI can support vulnerable communities responsibly. That experience reinforced a broader principle. AI is most valuable when it strengthens systems people depend on daily, whether healthcare or cybersecurity.

Data protection is not glamorous, but it underpins trust in every digital interaction.

Investors often chase visible innovation. Why focus on infrastructure that works quietly in the background?

Because infrastructure compounds. When enterprises standardize on a security layer that integrates across collaboration tools, cloud applications, and AI platforms, switching costs increase and value deepens over time.

Flashy innovation attracts headlines. Infrastructure earns renewals.

“In AI investing,” Singi, a distinguished member at Z21 Ventures, emphasizes, “durability matters more than excitement. The strongest bets are the ones enterprises cannot afford to reverse once they are deployed.”

As AI continues to reshape enterprise operations, the capital that endures will likely be capital placed behind systems that defend, stabilize, and secure. In that sense, decision-grade investing is less about predicting the next breakthrough and more about recognizing which problems have already become unavoidable.