From the Chip to the Agent: End-to-End Trust for On-Prem and Cloud Deployment

AMD + Anjuna: Built for the AI Wave

‍

Agentic AI systems make autonomous decisions, call external tools, and access sensitive data at machine speed, often without human oversight. Without supervision, a compromised agent can exfiltrate data and cause lateral damage faster than any incident response team can react.

‍

Anjuna Overwatch governs autonomous agents in real time, enforcing context-aware policies that control tool use, and data access based on context and goals, and ensuring agents operate only within sanctioned boundaries. Running and attested inside a Trusted Execution Environment (TEE) powered by Anjuna Seaglass, it acts as an incorruptible protected control layer to deliver compliance and risk mitigation required by enterprise security and regulatory compliance policy. The Anjuna Seaglass capability to easily secure workloads on AWS, Azure, and Google Cloud has been extended seamlessly to AMD SEV with Secure Nested Paging (SNP) hardware in data centers.

‍

Anjuna Seaglass leverages AMD SEV to provide hardware-enforced memory encryption and isolation at the workload level. Security and platform teams gain an additional layer of protection for containerized applications running on bare metal with the same policy model, attestation workflow, and operational experience they use across their cloud deployments.

• Hardware-enforced governance “from the silicon up”: Together with AMD SEV-SNP, Anjuna Overwatch provides the industry’s first trusted control plane built on confidential computing to govern AI agents so that a compromised host, a rogue admin, or the agents themselves can’t tamper with the policies that constrain them.
• Ensure AI agents operate only within sanctioned boundaries: Autonomous agents act on their own, so they can reach data, tools, and systems no one approved. Enforce policy at runtime so each agent can access only the models, data, and actions you’ve authorized, and block anything outside those limits before it happens.
• Visibility into usage to manage cost allocations: See which agents, teams, and workloadsare consuming which models and resources. Attribute AI spend to the right business unit and catch runaway usage early, so cost governance doesn’t depend on after-the-fact billing reviews.
• Compliance-ready, with auditor-grade evidence: Remote attestation gives auditors verifiable evidence that the right policy engine was running on genuine hardware, to comply with industry regulations like HIPAA, PCI-DSS, GDPR, DORA, FedRAMP, and sovereign AI mandates.