Unbound Secures $4 Million Seed Funding to Enable Secure and Controlled AI Adoption in Enterprises

Unbound raises $4 million in seed funding to help enterprises securely manage AI adoption. Its AI Gateway offers real-time data protection, model routing, and usage analytics, preventing data leaks and reducing costs. Backed by leading investors, Unbound enables organisations to control AI tools safely and efficiently.

Unbound, a startup focused on enterprise AI security and management, has raised $4 million in an oversubscribed seed funding round. 

The round is led by Race Capital, with participation from Wayfinder Ventures, Y Combinator, Massive Tech Ventures, Alpha Square Group, Northside Ventures, Liquid2, Pioneer Fund, Scale Asia Ventures, SBXI, and prominent angel investors including Ram Shriram (founding board member at Google), Dr. Trishan Panch (CSO LuminHealth), Dr. John Brownstein (Chief Innovation Officer, Boston Children’s Hospital), and others from Silicon Valley and cybersecurity sectors.

Addressing the challenge of shadow AI in enterprises

Generative AI tools are rapidly becoming a standard part of enterprise workflows. Employees use AI copilots to assist in coding, drafting documents, brainstorming, and data analysis. However, this bottom-up adoption often occurs without the knowledge or approval of IT departments. Consequently, enterprises lose visibility and control over sensitive information handling, the AI models in use, and access permissions.

Unbound provides a solution by offering IT teams a platform to introduce and manage AI tools safely and effectively. Its core product, the AI Gateway, integrates with popular AI applications such as Cursor, Roo, Cline, and internal document copilots. The Gateway enables real-time data protection, dynamic model routing, and detailed usage analytics. This approach prevents sensitive data leaks, optimises costs, and ensures that AI adoption aligns with organisational policies.

Founders with deep security and engineering expertise

Unbound’s founding team brings extensive experience in enterprise security and software infrastructure. CEO and co-founder Rajaram Srinivasan has a background in data security product leadership at Palo Alto Networks and Imperva, along with early SaaS security work during the rise of AI technologies. Co-founder and CTO Vignesh Subbiah is an engineer with a proven track record scaling teams and platforms from early to growth stages, previously part of the founding teams at Tophatter and Shogun. The duo previously collaborated at Adobe before launching Unbound to tackle emerging security gaps in AI workflows.

Proactive protection without hindering productivity

As early as the GPT-3.5 era, Unbound observed teams inadvertently sharing sensitive prompts within AI tools, resulting in secret leakage, exposure of personally identifiable information (PII), and uncontrolled usage of costly AI licenses. Traditional Data Loss Prevention (DLP) solutions either blocked AI tools entirely or failed to accommodate evolving AI use cases.

Unbound’s platform differs by redacting sensitive content in real time and rerouting high-risk requests to secure, internal open-source models hosted within the enterprise cloud. This method prevents data leaks while allowing employees to continue their work uninterrupted. The company reports prevention of hundreds of secret credential leaks, including passwords and API keys, as well as over 500 incidents involving PII such as customer details and patient records.

Cost control and flexible model access

Unbound enables enterprises to allocate AI model usage based on workflow criticality. Premium models serve high-stakes functions like core infrastructure engineering, while lighter tasks like content editing utilise smaller open-source models. This selective routing has allowed mid-market customers to save upwards of $10,000 annually by avoiding unnecessary premium AI seat licenses.

When new AI models outperform existing ones, as recently seen with Gemini 2.5 surpassing Claude Sonnet for specific coding tasks, Unbound facilitates incremental rollout. IT teams can test and swap models without disrupting employee workflows, providing a smooth transition to improved AI capabilities.

Growing adoption across sectors

Unbound’s platform is already deployed by multiple mid-market and enterprise clients in technology, healthcare, and other sectors. A leading technology company recently adopted Gemini 2.5 for over 100 engineers within a single week, using Unbound to ensure security and operational continuity.

Rajaram Srinivasan, CEO of Unbound, emphasises the importance of enterprise flexibility and control: “As AI tools become mainstream, enterprises are turning to flexibility and control. They want visibility into what’s being used, assurance that their data is protected, and the ability to swap in better models as the space evolves. Unbound is the bridge that makes that possible.”

Vignesh Subbiah, CTO and co-founder, highlights the company’s security-first approach: “Defaulting to blanket bans on AI tools is like being in the times of GPT 3.5. Unbound enables surgical security controls into every AI request so teams can innovate freely without putting corporate secrets at risk.” He adds, “In just a few months, our customers have prevented over 7,000 potential data leaks and cut AI tooling costs by nearly 70 percent.”

Industry perspectives and market outlook

The AI landscape is evolving rapidly from informal “shadow IT” to critical enterprise infrastructure. Chief Information Officers (CIOs) and Chief Information Security Officers (CISOs) seek solutions that allow widespread AI adoption without compromising security or governance.

Abraham Ingersoll, Chief Information Security Officer at The Hut Group (THG), a customer of Unbound, notes: “At THG Ingenuity, we see the security team as an enabler, not a blocker. Unbound empowers us to roll out AI tools to employees with confidence. Unbound AI Gateway’s data protection controls and intelligent routing have been instrumental in safeguarding sensitive data while helping us optimise costs.”

Edith Yeung, General Partner at Race Capital, points to the economic significance of AI adoption: “AI is projected to reach $4.8 trillion in market value for the enterprise by 2033 globally, but without proper guardrails, that value is at risk. From shadow models to data leaks, the dangers of unmanaged AI are very real. We are excited to back Rajaram, Vignesh and the Unbound Security team as they create a new category of AI infrastructure: one built for safety, observability and cost discipline from day one.”