DataKrypto

Decades of business know-how are becoming part of your AI agents and models. How are you protecting that knowledge while AI puts it to work? Deloitte’s latest article highlights DataKrypto’s role in answering that question. As enterprises embed their expertise, decision rules and proprietary processes into AI, they create what the article calls “Capital Intelligence”: business knowledge that becomes an executable competitive asset. Protecting that asset requires security that extends into AI execution. Encryption at rest and in transit leaves a critical exposure when data and models are decrypted for processing. DataKrypto extends encryption to data and models while they are in use, helping enterprises put their intelligence to work while protecting the knowledge behind it. Deloitte identifies this Confidential AI approach as a natural complement to Solaria, its agentic AI platform. For business leaders, the implication is clear: protecting the intelligence behind your AI is part of protecting the return on your investment. Read Deloitte’s perspective: https://lnkd.in/gcw-dZ8s Explore the full analysis on our site, including where enterprise intelligence is exposed and how to protect it: https://lnkd.in/gkasJdP2 #ConfidentialAI #AgenticAI #AISecurity

What happens when the software operating an AI pipeline can also reach the sensitive information moving through it? Recent incidents and research from OpenAI offer a glimpse of the challenges enterprises may face as AI agents become more autonomous. Permissions, sandboxes, and monitoring remain essential, but each can fail. When they do, the agent may expose the sensitive information it can read. In our latest blog, Paolo Campoli looks at why access and readability are not the same thing, and how encrypted computation can limit what an agent can expose when it takes an unexpected path. Read the blog: https://ow.ly/c0AY50ZTzxz

How do you maintain confidentiality when an agentic workload crosses infrastructure boundaries? Telecom networks are becoming more autonomous, with AI workloads moving across models, agents, tools, and infrastructure. An agentic workload may extend across data centers, edge environments, cloud platforms, and other infrastructure. Trusted execution environments remain valuable for establishing trust in specific infrastructure, protecting cryptographic material, and verifying an execution environment. The challenge is keeping data and models confidential as the workload moves between those environments. In our new blog, SVP & Chief Growth Officer Paolo Campoli explains why the enclave is the wrong shape for an agentic workload and why confidentiality must move with the workload. Read the blog: https://ow.ly/EYwl50ZLHMJ

What are you willing to trade for AI capabilities? AI is changing what it means to protect enterprise data. Proprietary knowledge that was once fragmented across databases and applications can now be concentrated into AI models, making those models high-value targets. At the same time, AI creates continuous exposure as sensitive data is processed in plaintext during training, inference, and fine-tuning. But that tradeoff isn’t inevitable. Read the blog: https://ow.ly/1I1f50ZB1Jv

For decades, enterprise security has been built around one principle: limit access. Agentic AI complicates that principle in a fundamental way. The more context an AI agent can access, the more useful it becomes. That means enterprises are giving agents broader access to sensitive data while trying to enforce security models originally designed to restrict that access. In an agentic architecture, sensitive data moves through an entire workflow: from inference, through retrieval pipelines, into persistent memory, and across agent-to-agent communication. Perimeter controls were never designed to follow it that far. Read our new blog: https://ow.ly/eyoX50ZhBg3

This week is Data Privacy Week, a great time to reflect on how organizations think about protecting data in an AI-driven world. As AI adoption accelerates and sensitive data flows through AI systems and analytics pipelines, data privacy is becoming more difficult to maintain using strategies built for yesterday’s architectures. Protecting data only at rest or in transit is not enough. The real challenge now is safeguarding data throughout its entire lifecycle, including when it is processed by AI and advanced analytics. In this clip, our CEO Ravi Srivatsav shares why data privacy has become a strategic priority and how continuous encryption is reshaping secure AI. #DataPrivacyWeek #DataSecurity #DataPrivacy #SecureAI #ContinuousEncryption