NIST SP 800-207

Zero Trust for AI Agents: Why Separate Identity Alone Is Not Enough

Denis Korbakov, CTO of Smart-Soft, explains why traditional IAM approaches fail to secure autonomous AI agents that dynamically select tools, change context, and delegate authority. Only 21.9% of teams treat agents as distinct identity-bearing entities, while 45.6% rely on shared API keys and 44.4% use generic tokens. Research from Gravitee, Cloud Security Alliance, and Aembit shows that 68% of organizations cannot distinguish AI agent actions from human actions, 74% grant excessive privileges, and 52% allow rights inheritance. The article maps NIST SP 800-207 Zero Trust principles—explicit verification, least privilege, and assume breach—to agent workloads using short-lived scoped tokens, SPIFFE/SPIRE credentials, and layered policy enforcement points. A concrete ticket-diagnosis scenario illustrates how prompt injection can be contained through per-task authorization, dedicated network segments, and independent telemetry from NGFW and SIEM. The piece concludes with an open question on sub-agent delegation chains and offers reference OPA/Rego policies plus runbooks for pilot implementations.

Why Deep Packet Inspection Overestimates Its Reach in Encrypted Networks

Modern encryption has fundamentally limited the effectiveness of Deep Packet Inspection systems, leaving network monitors with only metadata and behavioral patterns rather than actual content. DPI tools can still classify traffic types and apply policies based on visible flow characteristics, but they cannot read messages, files, or credentials inside properly encrypted sessions without explicit TLS inspection. The article details how TLS 1.3, Encrypted Client Hello, and QUIC further reduce passive visibility while corporate inspection remains possible only when endpoint devices trust an organizational certificate. Russian regulatory requirements around TSPU systems are discussed separately from corporate DPI use, with emphasis on the need for technical confirmation rather than assumptions. The piece also clarifies distinctions between DPI, IDS, IPS, and DLP, and explains why machine learning cannot convert metadata into decrypted payloads. Overall, the analysis shows that DPI remains useful for traffic management and known-threat detection where visibility exists, but it cannot serve as a complete security foundation.