Skyflow, the leader in data security for the modern AI data stack, unveiled its Runtime AI Data Security platform for AWS AgentCore, enabling enterprises to move agentic AI workflows from prototypes to production without compromising security, privacy, or regulatory compliance.
As AI models evolve rapidly and the industry invests hundreds of billions in AI infrastructure, many organizations struggle to transition from demos and prototypes to production-grade agentic applications. In response, Skyflow provides a secure, scalable solution that protects sensitive customer data while supporting complex AI workflows.
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Enterprises, startups, and ISVs deploying agentic applications need reliable ways to prevent data leakage, meet global compliance requirements, and enforce which data an agent can access in specific contexts. Traditional data security tools and AI security posture management systems often block or redact sensitive data, preventing agents from completing automated workflows effectively. In contrast, Skyflow removes these roadblocks by protecting data in use with fine-grained controls, ensuring sensitive customer data remains governed, auditable, and secure even during active processing.
AWS and Skyflow: A Secure Partnership
AWS recently introduced Amazon Quick Suite and Amazon Bedrock AgentCore, its agentic AI offerings designed to help organizations build, deploy, and operate intelligent agents securely at scale. Skyflow’s Runtime AI Data Security platform integrates seamlessly with these solutions, delivering runtime, field-level, contextual, and identity-aware protections for sensitive data, including PII, PHI, and PCI.
For instance, with Amazon Quick Suite, Skyflow inspects and de-identifies sensitive data before it reaches agents or models, maintaining compliance with GDPR and HIPAA. Meanwhile, on Amazon AgentCore, it enforces policy-based, minimum-necessary access with field-level logging for complete auditability.
“Agentic AI is rewriting how enterprises use data, but it demands a new level of protection when autonomous agents interact with sensitive information. At AWS, security has always been our top priority—it’s the foundation of everything we build. AWS Quick Suite and AgentCore enable organizations to operationalize AI at scale, and integrating Skyflow adds the fine-grained data protections and agent-level governance required in regulated industries. This combination helps customers move from pilots to production with confidence, knowing their most sensitive data remains protected at every step,” said Andy Perkins, General Manager, US ISV Sales – Data, Analytics, GenAI.
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Unlocking Agentic AI Workflows
As AI shifts from single LLM calls to multi-step autonomous agents, enterprises face the challenge of granting agents access to sensitive data while maintaining compliance. Skyflow’s platform addresses this by embedding a privacy-trust layer directly in the AI data flow, powered by its patented polymorphic engine.
Key features include real-time discovery, classification, and de-identification of sensitive data, fine-grained rehydration for specific workflow tasks, global data residency enforcement, context-aware governance, and entity-preserving transformations like secure embeddings. Every access, transformation, and data rehydration event is logged for auditability and regulatory compliance.
“The success of agentic AI depends on absolute trust and data security when agents handle sensitive enterprise data. Skyflow’s Runtime AI Data Security provides the essential guardrails—protecting data exactly at the moment of use. This enables enterprises to safely scale from cautious AI pilots to full production, supporting the AWS agentic AI mission,” said Anshu Sharma, CEO, Skyflow.
By combining AWS Quick Suite, AgentCore, and Skyflow, enterprises can finally unlock responsible, secure AI innovation, moving agentic AI workflows safely from prototype to production at the speed of AI.
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