Ameesh Divatia is the co-founder & CEO of Baffle, an organization focused on integrating data security into every aspect of the info pipeline to simplify cloud data protection and minimize the impact of information breaches.
Its platform offers a no-code, easy-to-deploy solution that secures sensitive data without affecting performance or requiring changes to applications. Baffle’s technology is compatible with major cloud providers corresponding to AWS, Azure, IBM, and GCP. Serving a wide selection of clients, from Fortune 25 firms to small and medium businesses, Baffle protects over 100 billion records worldwide, working with system integrators for efficient deployment.
What motivated you to co-found Baffle, Inc., and the way did your previous entrepreneurial experiences shape your approach within the early stages of the corporate?
After my last company’s exit, I took a much-needed break to recharge and take into consideration what I actually desired to do next. I’ve at all times loved constructing firms, so I began having conversations with an early-stage VC friend of mine, and he introduced me to Priyadarshan “PD” Kolte, who would turn into my co-founder. He challenged us with an intriguing query, disguised as a challenge: “How do you get value from data while still protecting it?” That challenge hooked me—solving tough problems is what I live for. There was a glaring gap in data protection, especially around simplifying encryption and protecting data ‘in use’. Nine years later, here we’re, answering that query with Baffle.
With the rise of generative AI, how can firms make sure that their data stays secure while still leveraging the advantages of AI technologies?
This can be a query every company dabbling in AI should be asking. Security and innovation often feel like two opposing forces, but they don’t need to be. The secret is a breakthrough innovation called Privacy Enhanced Computation (PEC) that begins with encryption—keeping data protected at rest, in transit, and while in use. By encrypting sensitive data before it gets to the AI models first after which using PEC to process it, you’ll be able to still get the insights you wish without compromising security. It’s about staying ahead of the sport, updating security protocols, and leveraging tools like Baffle to mitigate risks. You don’t need to sacrifice innovation for security.
Are you able to explain the particular role of encryption in protecting AI-generated data and models? How does it differ from traditional data protection methods?
Encryption for AI data is like wrapping your most dear asset in bubble wrap—irrespective of how much it’s tossed around, it stays protected. Consider it as locking the info when you’re using it. Traditional methods give attention to securing data when it’s not in use (at rest) or when it’s moving (in transit). But with AI, we’re adding a brand new layer of complexity because the info needs to remain encrypted even when it’s being crunched by models. Baffle focuses on this “data-in-use” protection, ensuring performance isn’t impacted but security isn’t sacrificed.
Baffle recently launched an information protection solution specifically for GenAI projects. Are you able to share more details about how this solution works and what makes it unique available in the market?
Our GenAI solution is all about making encryption easy and efficient, even once you’re working with AI. It plugs into an existing AI pipeline by protecting data because it is being ingested. That is followed by a capability often known as real-queryable encryption that processes the info without exposing it. Most significantly, you don’t need to alter anything in your AI pipeline—no rewriting code, no hassle. Just plug it in and go. We’ve focused on ease of use and ensuring security doesn’t get in the way in which of innovation, which is why customers are finding this solution so attractive.
Your platform emphasizes “no code” changes for implementing data protection. How does this approach profit firms, especially those with large, complex data pipelines?
Nobody wants to interrupt something that’s already working. With our “no code” approach, firms don’t have to rip apart their existing applications or data movers so as to add encryption. This can be a huge profit for big organizations with complex data pipelines since it means they’ll bolster security without risking disruptions. It’s faster, easier, and removes a number of the headaches that typically include integrating latest tech.
How does Baffle’s Real Queryable Encryption differ from other encryption methods, and what benefits does it offer for firms handling large-scale data analytics?
Real Queryable Encryption is our secret sauce. Unlike traditional encryption, which requires you to decrypt data in the info store before analyzing it (and thereby exposing it), we allow you to run queries on the encrypted data itself. It’s like having your cake and eating it too—you get the insights without risking security. This can be a game-changer, especially for firms coping with huge amounts of sensitive data, like in finance or healthcare, where compliance is non-negotiable.
Data-in-use protection is a critical feature of Baffle’s platform. Are you able to explain how this works and why it’s essential for firms, particularly within the context of GDPR and other data privacy regulations?
When data is in use—being processed by systems—it’s normally at its most vulnerable. That’s why protecting it in real-time is critical, especially with regulations like GDPR, which require a posture often known as ‘data protection by design’. Our platform ensures that even when data is being processed, it’s still encrypted. This approach eliminates that dangerous window of exposure where data breaches often occur, helping firms stay compliant and secure.
As AI models turn into more complex, what are the essential challenges in securing these models against adversarial attacks, and the way does Baffle address these challenges?
AI models are getting smarter, but so are attackers. Adversarial attacks—where bad actors try to control the info that impacts an AI model’s output—are a growing concern. We tackle this by specializing in the info side. By encrypting the info that the AI models depend on, we make it much harder for anyone to mess with the model’s integrity. It’s like giving the AI model a locked vault of information—nobody’s getting in without the important thing.
Are you able to discuss the importance of role-based access control (RBAC) in modern data protection strategies, especially for organizations using multi-tenant cloud environments?
In multi-tenant cloud environments, RBAC is essential. Imagine you’ve got a bunch of individuals all sharing the identical cloud infrastructure. Without RBAC, it’s like giving everyone access to the entire constructing as an alternative of just their office. Our platform integrates RBAC so only authorized people based on their individual role or credential, can access sensitive data, keeping things locked down tight and reducing the chance of breaches.
Baffle has seen significant growth lately, together with your revenues doubling up to now yr. What do you attribute this growth to, and the way do you intend to proceed this trajectory?
We’re riding a wave of demand because we’ve built the suitable solution for the suitable problem. Our growth comes all the way down to one thing: we’re solving an issue that each company faces—data protection. With cyber threats on the rise and regulations getting tougher, firms are turning to us for solutions that work without slowing them down. Our give attention to real-queryable encryption and ease of use is a giant reason for that growth. Moving forward, we’re planning to maintain pushing the envelope on innovation, expanding our products, and constructing strong partnerships that take us into latest markets.
