Cyberhaven raises $88M to enhance AI-powered data security is profiled by BTW Media because public-source evidence links it to internet infrastructure, governance, operational dependencies, or market visibility.
Controlled classification for comparative analysis.
Primary geography where strategy signal is most visible.
Principal area tracked in this profile.
Structured profile with operational and governance relevance.
Domain interpretation lens.
Session topic under controlled profile taxonomy.
Leadership and execution signals affect strategy timing.
| 0.90–1.00 | A | High — direct sources |
| 0.75–0.89 | A/B | Strong |
| 0.55–0.74 | B/C | Medium |
| 0.35–0.54 | C/D | Weak–medium |
| 0.10–0.34 | D | Weak signal |
| 0.00–0.09 | D | Internal monitoring |
Mixed-source
- Cyberhaven secures $88 million in Series C funding led by Adams Street Partners and Khosla Ventures.
- The funding will support the expansion of Cyberhaven’s AI-driven data security platform.
- Notable security experts including Amol Kulkarni, Gerhard Eschelbeck, and Ash Devata also invested.
- Cyberhaven reports a 200% increase in new bookings this year.
OUR TAKE
The infusion of $88 million into Cyberhaven is a clear testament to the urgent need for advanced data protection solutions. The company’s innovative use of AI to understand and secure data is not only timely but essential in today’s technology-driven environment. This funding marks the beginning of an exciting new chapter for Cyberhaven, promising significant advancements in how we protect our most valuable digital assets.
Dudu, BTW Reporter
Cyberhaven secures $88 million (£69 million) in Series C funding, aiming to revolutionise data security with advanced AI-driven technology.
AI-driven data security startup raises significant funds
Cyberhaven, a pioneer in data detection and response (DDR) technology, has raised $88 million in a Series C funding round. Leading the round were Adams Street Partners and Khosla Ventures, with significant participation from existing investors like Redpoint Ventures and Costanoa Ventures.
Cyberhaven’s innovative approach involves using AI to trace data lineage, offering a level of protection that traditional data loss prevention (DLP) and insider risk tools often miss. The company’s technology can identify and understand the context of sensitive information, such as source code and customer records, to ensure precise classification and protection.
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Growing importance of advanced data protection
Fred Wang of Adams Street, now a Cyberhaven board member, praised the company’s methods. “Cyberhaven is tackling critical data security challenges, especially protecting intellectual property from insider threats. Their use of AI to comprehend and secure data is groundbreaking.”
Howard Ting, CEO of Cyberhaven, highlighted the company’s ambitions: “This funding will accelerate the expansion of our platform, allowing us to protect more innovators reliant on AI and data. Our data lineage technology has significantly enhanced our customers’ DLP and insider risk programs, but we’re only at the beginning.”
Investing in the future of data security
Cyberhaven plans to utilise the new capital to diversify its product offerings, increase market presence, and continue protecting the most sensitive data globally. This funding round marks a significant milestone in the company’s journey to redefine data security standards.
A promising outlook for cyberhaven
The considerable investment reflects growing confidence in Cyberhaven’s potential to transform the data security landscape. By focusing on AI and data lineage, Cyberhaven is set to play a crucial role in protecting intellectual property and sensitive information in an increasingly digital world.
Core Entity Brief
- Entity: Cyberhaven raises $88M to enhance AI-powered data security
- Subject Type: Internet infrastructure institution
- Region: Asia Pacific
- Classification: Institution Type
Service Surface / Control Surface
- Public records support monitoring of governance, service, and infrastructure control surfaces.
Governance and Policy Surface
- Public-source signals support medium-impact monitoring for infrastructure visibility and dependency analysis.
- Operational criticality: Medium
- Time horizon: Quarter (30-120d)
Decision Trigger Matrix
- Monitoring focuses on verified service continuity, governance changes, and relationship signals.
Current state favours active tracking due to infrastructure relevance.
Public-source signals support medium-impact monitoring for infrastructure visibility and dependency analysis.
Long-cycle infrastructure decisions likely to remain path-dependent.
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