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AvePoint banks on AI governance and dual listing to escape AI stock volatility

Nurdianah Md Nur
Nurdianah Md Nur • 7 min read
AvePoint banks on AI governance and dual listing to escape AI stock volatility
Jiang believes AvePoint’s strong fundamentals and AI governance momentum will position it to rise above the sentiment-driven swings hitting AI-linked stocks. Photo: AvePoint
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Security gaps are emerging as the biggest brake on enterprise adoption of artificial intelligence (AI). Three-quarters of organisations using AI suffered breaches in the past year, and data security concerns stopped 69% of firms from expanding deployments, according to research by AvePoint. Dual-listed on Nasdaq and the Singapore Exchange (SGX), AvePoint provides data management and governance tools.

As organisations grapple with these risks, demand is growing for tools that tighten data access and standardise AI oversight. AvePoint reported 3QFY2025 software-as-a-Service (SaaS) revenue — which includes its AI governance and data security platforms — of US$84 million ($109.9 million), up 38% y-o-y. Total revenue reached US$109.7 million, up 24%, while annual recurring revenue climbed 26% to US$390 million. Dollar-based net retention of 110% indicates that existing customers are expanding their governance deployments.

“Our platform-driven approach to governing and securing data, coupled with our ongoing innovation (especially as agentic AI intensifies existing data management challenges) uniquely positions AvePoint to lead,” Tianyi Jiang (TJ), CEO and co-founder of AvePoint, tells The Edge Singapore.

Southeast Asia, he adds, will continue to be a key focus as the region’s digital economy accelerates. “Asean is going to be the economic growth engine of the world in the next five to 10 years… and [may] very soon exceed the EU economy,” he says. Singapore anchors that strategy as AvePoint’s Asia Pacific hub, international headquarters and R&D centre, contributing more than 10% of the company’s revenue.

Shadow AI surges despite policy push

Even as companies write formal AI rulebooks, employees are increasingly going rogue. The use of unsanctioned AI tools (or software adopted without the IT team’s approval) grew from 23% of employees a year ago to 30% today, and is projected to reach 36% within 12 months, according to AvePoint’s research.

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The rise persists despite a surge in corporate policies. The use of AI acceptable use policies increased 80% y-o-y, with 85% of organisations enforcing or developing guidelines and nearly all expecting formal policies within a year. Yet, most lack visibility into actual risks, with 90% relying on basic usage reports that track adoption rather than security threats.

Analysts warn the gap could be costly. Gartner estimates that 40% of AI agent projects could be abandoned by the end of 2027 due to inadequate safeguards. This forecast underscores the market need for specialised governance providers such as AvePoint.

Multi-cloud complexity compounds risk

See also: Singapore embraces AI, but workforce sentiment remains a challenge

Oversight is further complicated as enterprises shift from a single provider to a multi-cloud environment. That fragmentation makes it difficult to track where data lives, which AI tools are accessing it and how much each IT environment is costing.

To address this, AvePoint recently unveiled AgentPulse Command Center. The tool gives companies a single view of their AI agents, showing which ones are active, who is using them and what sensitive data they are touching. Hyperscalers, Jiang argues, cannot provide this visibility across rival platforms, leaving companies to stitch together their own view of risk.

AgentPulse Command Center also flags AI agents generating unusually high volumes of activity or data usage, helping companies shut down redundant or risky tools before bills escalate. This cost-control pitch has resonated with companies, especially after incidents where breaches sent cloud bills soaring with no recourse from cloud providers. Jiang recalls incidents last year where hackers tapped customers’ cloud resources. “Even though those customers’ cloud utility bill spiked, none of the [affected cloud provider] will pay for that.”

He also highlights AvePoint’s industry-specific expertise as critical for managing AI in a multi-cloud environment. “Even today, we help many large organisations put guardrails around AI agents such as controlling what the AI agent can talk to, what data it has access to, what permission it has and how long it lives.”

Regulatory work builds credibility

AvePoint’s governance positioning is strengthened by its work on the Collaborative Sharing of Money Laundering/Terrorism Financing Information and Cases (Cosmic) digital platform. The company built Cosmic with the Monetary Authority of Singapore and six major banks, namely DBS, Oversea-Chinese Banking Corporation, United Overseas Bank, Citibank, HSBC and Standard Chartered.

Cosmic allows banks to share anti-money laundering data when customer profiles display red flags, while maintaining confidentiality safeguards. The platform uses AvePoint’s solutions, enabling it to use machine learning and AI algorithms that can detect certain anonymous patterns. It addresses three key financial crime risks in commercial banking: misuse of legal persons; misuse of trade finance for illicit purposes; and proliferation financing.

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Now in its second iteration, Cosmic has drawn interest from other governments. “It will become more and more like a national-level priority to have better control because the only way to actually do better [at preventing money laundering and financial crime] is to have more and better data,” says Jiang.

Besides Cosmic, AvePoint has helped many major US banks deploy Microsoft Copilot and AI agents, demonstrating its technical capabilities in AI governance and a deep understanding of financial services’ regulatory requirements.

Dual listing targets mid-cap volatility

Despite intense enthusiasm for AI, growing concerns about an AI bubble are weighing on markets. The tech-heavy Nasdaq fell 2% on Nov 21, erasing earlier gains as investors signalled scepticism about the AI boom’s longevity.

According to a Bank of America survey in November, 45% of investors cited the “AI bubble” as the biggest “tail risk” for the economy and markets, while 53% believe AI stocks are already in a bubble.

The turbulence has hit pre-mid-cap tech companies, regardless of fundamentals. “In the last few months, or actually this whole year, we’ve seen mid-cap, pre-mid-cap cap and small-cap tech companies showing quite a bit of volatility. The market sentiment is no longer fundamentally driven,” notes Jiang.

AvePoint has not been spared. When it debuted on SGX on Sept 19 as the first B2B SaaS firm on the exchange and the first company dual-listed on both Nasdaq and SGX, shares opened at $20.20 and closed at $19.70. The stock now trades below that level. “We think we are kind of almost unfairly bucketed into this ‘Oh, they’re only doing well because AI’ [perception],” adds Jiang.

However, the company’s fundamentals tell a different story. AvePoint delivered GAAP profitability a year ahead of schedule, raised its full-year ARR forecast to $412.8 million–$418.8 million, expanded non-GAAP operating margin to about 18.6%–18.8%, and cut sales and marketing costs to 30% of revenue from 44% at its 2021 IPO. The company also holds $472 million in cash and short-term investments with zero debt.

While AI is a tailwind, Jiang emphasises that it is “not the necessary and sufficient condition for success”. He views achieving mid-cap status as critical to escaping sentiment-driven volatility. “We need to get to mid-cap as fast as we can. That’s when we escape the velocity of being so volatile.”

AvePoint is relying on multiple strategies, including its dual listing, to reach that goal. The three-times oversubscribed SGX offering brought in long-only institutional investors while reducing insider ownership, with no new capital raised.

Jiang says: “The dual listing is a great way to get exposure and awareness, and also pick up very high-quality, long only institutional investors with an Asia Pacific mandate. As we continue to [execute our] profitable growth strategy to go from pre-mid-cap to mid-cap, we need to have really solid, long-only institutional investor support.”

Beyond that, the company is preparing to deploy its US$472 million cash balance. “We’re looking to do larger [M&A] deal sizes than previously because we have a healthy balance sheet for it,” adds Jiang. Priorities include accelerating go-to-market in non-Microsoft cloud ecosystems, moving from unstructured to structured data security, and deeper channel investments.

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