Cloud-security expert Soumya Debabrata Pani argues the next frontier is protecting data in use바카라securing AI workloads, runtime environments, and trust at scale across public-cloud platforms.
Public-cloud security has travelled far in a decade바카라but not, says Soumya Debabrata Pani, far enough. The Microsoft-and-Google veteran argues that the industry바카라s real test now lies in protecting data while it is being processed, not just when it sits on a disk.
바카라Encryption at rest solved yesterday바카라s audit question,바카라 Pani notes. 바카라The tougher question is: what happens to an AI model바카라s training data once it바카라s loaded into GPU memory?바카라
IBM바카라s 2024 Cost of a Data Breach study puts the global average impact of a breach at USD 4.88 million바카라a 10 percent jump in a single year. Such figures, Pani says, force boards to look beyond checkbox compliance.
바카라Regrettably, we still see security treated as a project completed at deployment. Modern attackers exploit runtime blind spots, not just unpatched servers,바카라 he explains.
Those blind spots are expanding. IDC forecasts public-cloud spending to hit USD 1.35 trillion by 2027, nearly double 2023 levels, as regulated workloads flood off-premises. 바카라Every new workload is a fresh piece of attack surface,바카라 Pani warns. 바카라If we don바카라t scale protections proportionally, costs will rise faster than adoption.바카라
Pani바카라s early work at Microsoft focused on automated credential management: rotating, vaulting and retiring passwords for millions of Azure hosts without human touch. The approach, he says, removed roughly 바카라80 percent of what an attacker typically tries first.바카라
But passwords are only the starting line. In 2020 his team introduced a fleet-wide BitLocker rollout that encrypted every byte written to disk, including legacy servers. That project바카라once deemed 바카라logistically impossible바카라 at hyperscale바카라has since become table stakes across major providers.
Today, Pani concentrates on privacy-enhancing computation (PEC) techniques at Google Cloud, especially for AI and machine-learning accelerators. Gartner predicts that roughly 60 percent of large organizations will adopt at least one PEC method by 2025 to meet regulatory and competitive pressure.
바카라Model weights are intellectual property; training data is often personal data,바카라 Pani says. 바카라Techniques like trusted-execution environments, federated learning and selective homomorphic encryption let you compute without exposing either.바카라
바카라Many architectures still hand off decrypted data to memory without attestation. Start by mapping where clear-text still appears inside the pipeline.바카라
바카라Hardware-rooted keys should follow the server바카라literally remain soldered바카라through its entire life-cycle.바카라
바카라Automation fails if engineers default to 바카라break-glass바카라 overrides. Enforce policy through code reviews and compensating controls, not memos.바카라
Pani is sceptical of any single 바카라silver-bullet바카라 technology. Instead, he likens modern cloud security to aviation safety: 바카라You fly because many imperfect safeguards overlap.바카라 Confidential-computing enclaves, differential-privacy overlays and post-quantum key hierarchies, he argues, must interlock rather than compete.
What keeps him engaged after a decade of 3 a.m. pager alerts? 바카라We바카라re building trust at industrial scale,바카라 he says. 바카라Everything people do online바카라banking, telemedicine, generative-AI diagnostics바카라assumes the cloud is a safe substrate. Our job is to make that assumption correct before reality proves it wrong.바카라
As regulators tighten rules on data sovereignty and AI ethics, and as enterprises increase investments in cloud infrastructure, the task of securing data in transit is becoming increasingly important in cloud evolution. Pani is already working on these challenges.
Soumya Debabrata Pani is an experienced cloud-security specialist whose career spans leadership roles at Microsoft and Google Cloud. With more than a decade of experience in public-cloud infrastructure and cybersecurity, Pani has worked on critical initiatives that enhance trust in cloud platforms, particularly through the integration of privacy-enhancing computation (PEC) techniques and runtime protection mechanisms.
At Microsoft, Pani led efforts in automating credential management across Azure, eliminating large classes of vulnerabilities through zero-touch rotation and vaulting. He later worked on the rollout of disk-level encryption using BitLocker across legacy and modern hosts바카라an initiative once considered logistically impractical at hyperscale by leading experts. His contributions helped standardize encryption-at-rest practices across major cloud providers.
Currently at Google Cloud, Pani focuses on securing AI and machine-learning workloads using confidential computing. His work explores advanced security technologies such as trusted-execution environments (TEEs), federated learning, and selective homomorphic encryption바카라methods designed to protect data while in active use, not just at rest or in transit. He is particularly concerned with protecting model weights and training data, which often contain proprietary and sensitive information.
Pani is a vocal advocate for treating runtime as a primary attack surface and for embedding security throughout the entire hardware and software lifecycle. His philosophy aligns with the belief that overlapping, multi-layered safeguards바카라rather than a single solution바카라are essential to maintaining trust at scale.
As enterprises accelerate cloud adoption and regulators demand tighter controls, Pani바카라s work focuses on the infrastructure underpinning secure, ethical, and resilient digital systems.