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Problem-Aware

202 posts on problem-aware.

69% of Enterprises Let AI Agents Share Credentials: The Attribution Problem That Starts Before the Breach

VentureBeat Q2 2026 agentic security research found that 69% of enterprises let AI agents share credentials, and 54% had already had an agent-related security incident or near-incident. With several agents on one key, the forensic trail goes cold at the credential level. This is an attribution problem an identity-aware gateway fixes before anything goes wrong, and this article walks through what per-agent identity and per-decision logging change about incident response.

agentic-aiai-securityidentity-and-authorizationforensic-auditai-governancellm-security
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Shadow AI Detection Tools: The Signals That Actually Surface Unsanctioned AI Use

Shadow AI detection draws on five signal families: network and DNS logs, CASB and OAuth consent grants, endpoint and browser telemetry, identity and SSO logs, and the AI gateway itself. This guide describes what each signal catches, where each goes blind, and why detection surfaces the problem while inline enforcement at the request boundary is what closes it.

shadow-aiai-securitydata-loss-preventioncybersecurityinline-enforcement
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The Einstein Trust Layer Audit Gap: What Its Log Covers and What Routes Around It

Salesforce''s Einstein Trust Layer writes a detailed audit record for AI interactions that flow through its LLM Gateway, prompt, masked prompt, completion, toxicity score, grounding source, user, and timestamp. That record is scoped to Salesforce-mediated traffic. This piece explains what the Trust Layer captures, why AI traffic that routes around Salesforce never appears in it, and what enterprise-wide per-decision audit requires.

forensic-auditai-securityai-governanceshadow-aicompliance
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Check Point's AI Security Report 2026: The AI Infrastructure You Cannot See Is Already Being Probed

Check Point Research published its AI Security Report 2026 on July 15, 2026. Most coverage focused on autonomous exploitation and deepfakes. The under-read section is the AI-infrastructure attack surface: exposed model servers, agent control panels, and inference endpoints that attackers probe while most organizations have no inventory of them. This is the visibility gap an identity-aware policy gateway closes on the request path.

ai-securitythreat-reportai-infrastructureinline-enforcementai-visibility
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The AI Agent Security Guide: Where the Controls Actually Live

AI agents plan, call tools, and make requests to models without a human in the loop for each step. Securing them spans host isolation, tool scoping, identity, and the model-call channel. This guide maps the full control surface, marks which layers sit outside an HTTP policy gateway, and shows where identity-aware authorization and per-decision logging on agent-to-LLM traffic do the work.

ai-agent-securityagentic-aiai-agent-identityai-audit-trailai-egress
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The Security Layer in Agentic AI Architecture Sits at the Model-Call Boundary

Most agentic AI architectures diagram the planner, memory, tools, and model, then add security as a wrapper around the application. That places enforcement above the layer where agent decisions become actions. The load-bearing security layer is the model-call boundary itself, where identity, policy, and audit apply to every request an agent makes to a model.

agentic-aiai-architectureai-control-planeinline-enforcementai-audit-trail
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Model Version Drift Changes the Policy Decision You Need to Review

Model version drift occurs when a deployment route, provider alias, prompt contract, or capability changes after an approval decision. A usable control record ties the deployed route and version context to the identity, data class, business purpose, and policy evaluated for each request. This article explains how change management and request-path evidence work together.

ai-governanceai-securityllm-securityauditpolicy-enforcement
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AI Model Monitoring Needs Request-Level Context

AI model monitoring needs more than provider availability and application latency. A useful program connects each model route to the requesting identity, data classification, policy version, prompt or response handling outcome, and accountable owner. This article separates model-quality monitoring from request-path governance and explains the evidence a security review can retrieve.

ai-governanceai-securityllmauditpolicy-enforcement
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AI Consent Enforcement Needs a Decision Record at Use Time

AI consent enforcement depends on a specific purpose, a data subject reference, and a policy decision made when an application sends data to a model. Privacy notices and consent registers establish context, but the operational test is whether a live request carries that context into an enforceable decision. This article defines that request-path evidence and the controls outside the AI boundary.

ai-complianceai-governancegdprpolicy-enforcementforensic-audit
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Browser Agent Risk Starts at the HTTP AI Request Boundary

Browser agents can read a page, carry session context, and send instructions to an LLM or tool. The security review needs to separate browser-local execution and credential abuse from the HTTP AI requests that carry user data and delegated intent. This article maps that boundary, the evidence it creates, and the adjacent controls that still need owners.

agentic-aiai-securitycybersecurityidentity-and-authorizationpolicy-enforcement
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AI API Key Sprawl Turns Access Into an Unknown

AI API key sprawl occurs when model credentials are copied into scripts, agents, CI jobs, notebooks, and vendor integrations without clear ownership or narrow scope. Inventory, short-lived workload identity, secrets management, per-route authorization, and audit evidence reduce the uncertainty around each model request.

ai-securityapi-securityapi-keysidentityzero-trust
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AI Agent Privilege Escalation Starts With Delegation

Agent privilege escalation appears when an agent receives authority broader than the user, workflow, or tool invocation requires. The operational fix begins with bounded delegation, per-action authorization, short-lived credentials, and evidence that preserves who requested an LLM action and which policy allowed it.

ai-securityai-agentsidentityauthorizationzero-trust
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