AI Risk Monitoring

AI-SPM

Identify AI-native risks across Azure AI resources: exposed endpoints, authentication gaps, missing content filters, and encryption gaps.

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AI Security Controls Audit2 risks
Resources
9
Risky configs
2
Quota at risk
1
text-embed-ada
Local auth enabled · keys exfiltratable
High
gpt4-prod-eu
Quota at 92% · capacity pressure
Medium
gpt35-support-bot
Network ACL: deny default
Low
Network & Auth Posture

Check public exposure and authentication settings on AI resources.

Content Filter Auditing

Confirm content filtering policies are active and blocking flagged content.

Quota & Encryption Checks

Track quota pressure and key management settings.

Guided Hardening

Clear, prioritized steps to close every posture gap.

Security Controls Audit2 RISKS
gpt4-prod-eu
Local Auth Disabled
Key-based auth is disabled. Entra ID managed identities only.
Network ACL: Deny default
Public network access requires explicit IP allowlisting.
text-embed-ada
Local Auth Enabled
API key auth is active. Keys can be exfiltrated without Entra visibility.
Network ACL: Allow default
Endpoint is reachable from any IP. Restrict to trusted CIDRs.
Security Posture

Reduce AI Attack Surface

Identify AI-native risks and publicly exposed endpoints to uncover misconfigurations and eliminate exposure before attackers target them.

Content Filter Policy

Audit content filter policies

See which deployments have content filter policies configured and track aggregate blocked requests. Identify unprotected deployments that may return unfiltered outputs in production.

Content Filter Policy Audit
gpt4-prod-euConfigured
Microsoft.DefaultV2
4 blocked requests (24h)
gpt35-turbo-batchConfigured
CustomPolicy-Batch
0 blocked requests (24h)
text-embed-adaNo Policy
No policy configured
text-embed-ada has no content filter policy. This deployment may return unfiltered outputs in production.
Additional posture checks

Configuration-level guardrails, covered end to end

Beyond network exposure, authentication, and content filtering, TENET checks two more configuration-level guardrails on every Azure AI resource.

Quota pressure

Flag deployments running close to their token or request quota, since capacity pressure is itself a posture risk — a throttled deployment can fail over to a less-governed resource.

Encryption and key management

Audit encryption-at-rest and key management settings on AI resources, including whether keys are customer-managed or Microsoft-managed and where they are stored.

Secure and optimize AI workloads at scale

Get complete visibility across your Azure AI stack from day one and eliminate AI risk with a single platform.

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