The challenge is not differentiating, it proves the value of this distinction in a crowded market where most sellers appear to be the same but they cannot offer.
1. Hackernoon: What is your company with 2-5 words?
Bruno CorticData safety everywhere
2. Why is it time for your company?
This industry is at a turning point where it created data growth, modernization of the cloud, and artificial intelligence adopts the challenges of data security and the vision that constitute great risks to institutions. with
For decades, traditional security methods focused on infrastructure and infrastructure oceans – the final point of the bottom, networks and systems – but these models cannot keep pace with the data scale via IAAS, Paas and Saas and now AI tubes. The following borders will be independent artificial intelligence agents who work independently on the institution’s data, communicate with each other, and make decisions. The possibility of access in this world, based on data sensitivity, entitlements, and the context of use, remains without a solution.
The basis security is found to meet this transformation. By combining architecture on the cloud scale and the data context of Amnesty International, the luxury lake provides the vision and control necessary to secure data through its life cycle and prepare institutions for the operational requirements of the systems driven by artificial intelligence.
3. What do you like in your team, and why are you the person who solves this problem?
What I appreciate most in our team is a common mindset – technical experience working with bias to work. We have built a merit culture where people are encouraged to make bold decisions, move quickly, and learn from what does not work.
Participated founders, Branava and Jenish, bring strong backgrounds to the products, systems and data security that constitute how we think about construction on a large scale. Our team comes from companies such as Cohesity, Rubrik, Sumo Logic, AWS and Croldstrike, where we saw how difficult it is to manage and secure data in complex fast -moving environments.
We also have a bias for learning and adaptation, especially with the sophistication of artificial intelligence. We are not afraid of trying or changing the direction when needed, as long as it helps us to reach the right result. This flexibility, associated with experience, is what makes this team completely suitable to solve a problem that does not stand.
4. If you don’t build your startup, what will you do?
I am creative – how I always work. I am not sitting, and I am not at my best without a project to chase. If I don’t build security for the rulings, I will build something else – not another company, because Bedrock is the company – but something. In my spare time, I make wine, become beer, work with wood, and go out whenever possible – on the road, camping, and blasphemy. I also build and tamper with my daughter: we are a symbol, three -dimensional printing, and studying languages together. I wired to create, whether it is a program or something actually.
5. At the present time, how to measure success? What are your standards?
Success measures develop with each of the company’s stages. Currently, what I am looking for and measuring is indications to suit the product market, preparing in early GTM, and talent.
project
- Speed release
- Suitable for the products market (gaps in RFPS, etc.)
- Nps (customer joy)
GTM
- Pipeline growth and pipeline health
- growth
- Net retention of dollars
Talent
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NPS employee
-
Revelation rate
6. In a few sentences, what did you offer?
We help companies discover, classify and classify data through cloud and saas environments and local environments. Our metadata Lake provides an open basis that supports cooperation between jobs, giving safety, data and governance teams, exchanging vision in data sensitivity, accessing, use, and proportions. It is integrated with third -party tools and supports multiple use cases, including DSPM, artificial intelligence model, preparedness for COPILOT decrease, AI Agence, and compliance. The result is a unified layer of context that allows the enforcement of policy and reduce risks without creating an operational or other silo friction.
7. What is the most exciting in your jar yet?
Our customers are not only looking to solve DSPM – they are preparing for the contract coming from data challenges, and many of them are led by artificial intelligence. DSPM is a relevant starting point, but what matters most is a comprehensive, open and developmental context that applies to jobs and cases of use. We see some advanced examples of this already – such as the biotechnology company that uses us to track the DNA sequence while moving via its cloud environment to protect the customer of the customer. Although we are dealing with PII, phi and other standard groups of sensitive data, our artificial intelligence models also illuminate-can be trained in specific data sets to identify complex assets and their context such as genetic data, chips designs, business secrets, and other forms of intellectual property of high value.
8. Where do you think your growth will be next year?
We expect growth to come from the use of dilated institutions data through independent systems – AI agents, Copilots, and models that require access to sensitive information without direct human supervision. With this transformation accelerating and data sizes continue to grow, institutions are looking for a way to overcome DSPM projects for one time and towards wider and continuous data governance. Using cases often begin with discovery and classification, but they quickly expand access to access, signs, life cycle management, and compliance automation. A major part of our value is to feed contextual data – allergies, use, and entitlements – to current cyber security tools such as SIEM, DLP and CNAPP, which helps these systems give priority to risks and work more effectively. Open architecture supports this type of expansion without creating new operating silos.
9. Tell us about the first client’s expectations and revenues paid over the next year.
One of our most interesting customers is the Great Saas provider that uses us over ten of the girls’ data in their environment via IAAS, Paas and Saas. They are a very technical store and use the basis for the first application programming interface to integrate it into multiple stores and workflow. For example, they have a large safety lake and automatically decorating data in the lake with sensitivity and risk context in the descriptive data lake that the foundation is built. They also built a DLP solution, the top of the descriptive lake, through the dedicated stagnation robot that monitors the changes in GDrive and link these changes in the context of risks in the descriptive data lake to notify users who offer sensitive data.
10. What is the biggest threat to you?
The challenge is not differentiating, it proves the value of this distinction in a crowded market where most sellers appear to be the same but they cannot offer. Many customers start a single use such as DSPM, but they are struggling to expand or support the needs of the wider institutions. Our focus on helping customers avoid this trap by giving them an open basis that can be expanded that can expand through difference and jobs. The real threat is that buyers end up in another silo, with a multi -year contract, get a restricted budget, solve one problem but create more estuary.
This start -up founder’s interview template is based on Hackernoon Nuster & CEO of David Smoke for startup founders.
