← All posts

Problem-Aware

192 posts on problem-aware.

Shadow AI vs Shadow IT: Why the Old Detection Stack Misses the AI Request Layer

Shadow IT is the SaaS subscription the security team did not approve. Shadow AI is the LLM the employee opens in the browser tab next to the approved SaaS. The two look similar to the procurement team. They differ at the detection layer the security team built. This piece walks through the four mechanisms shadow IT detection uses, why each one misses the AI request layer, what shadow AI detection has to read instead, and the inspection topology that closes the gap.

shadow-aishadow-itdiscoverycasbai-discoveryvisibility
Read post →

How to Find Shadow AI Inside Your Organization: A Five-Source Detection Pipeline

Shadow AI lives in the browser tab next to the approved SaaS. The detection stack the security team built for shadow IT does not surface the signal. This piece walks through a five-source detection pipeline (network egress, endpoint telemetry, IdP claims, expense aggregation, approved-route gap analysis), the joining identity that ties the sources together, and the prioritization framework for triaging the patterns the pipeline surfaces.

shadow-aidiscoverydetectionvisibilitycasbai-discovery
Read post →

Shadow AI Detection Methods: The Five Detection Surfaces and Why Three of Them Miss Most Real Usage

Shadow AI detection happens on five surfaces: endpoint agents, network DNS, SSL inspection, identity provider logs, and inline AI request proxies. Endpoint, DNS, and identity logs detect attempts to reach known AI vendor domains but miss prompt content and never see browser-based usage to unsanctioned tools. SSL inspection captures content but only where TLS-break infrastructure is deployed to the AI provider domains. Inline proxies on the AI request path see identity, classification, and policy state at decision time. The five surfaces differ in what they detect and when.

shadow-aiai-securityai-governanceinline-enforcementcomplianceaudit
Read post →

Shadow AI for CISOs: The Four Questions the Board Asks and the Records the CISO Has to Produce

Cloud Radix reports 90% of CISOs identify shadow AI as their top security concern for the year. Boards are now asking four questions that translate directly into operational records: which AI tools are in use, what data has flowed to them, what policy applied at decision time, and what was the exposure window. The CISO who can answer the four with contemporaneous records has discharged the operational duty. The CISO who reconstructs from logs after the fact has not.

shadow-aicisoai-securityai-governanceauditcompliance
Read post →

Employee ChatGPT Monitoring: The Inspection Points That Actually See Prompt Content (and the Ones That Miss It)

Employee ChatGPT usage produces five separable telemetry surfaces, and only two of them see the prompt content. Endpoint and DNS surfaces see the connection. SSL inspection and inline AI proxies see the content. SSO sees the sign-in but nothing after it. The combination of where the inspection happens and what the record contains decides whether the monitoring satisfies the operational requirement an auditor or a board would accept. Cloud Radix reports 77% of employees using unauthorized AI admit to pasting sensitive business data into prompts.

shadow-aichatgptmonitoringinline-enforcementai-governancedlp
Read post →

Enterprise AI Usage Policy Template: The Eight Sections That Survive Both Workforce Adoption and Regulatory Review

An AI usage policy that an enterprise can actually enforce contains eight sections: scope and definitions, sanctioned tools list, data classification rules, role-based permissions, the disclosure obligation, the inspection and monitoring statement, the incident reporting path, and the policy version history. A policy without inspection architecture behind it leaves the enterprise with a written commitment the workforce can ignore. The eight sections align with the EU AI Act Article 26 deployer obligations and the Article 12 record-keeping mandate.

ai-governanceai-policycomplianceshadow-aiauditai-security
Read post →

What Is Agentic AI: The Architectural Definition, the Control-Plane Implications, and the Audit Record It Requires

Agentic AI is a software pattern where an LLM-driven agent decomposes a goal, calls tools, observes results, and iterates until the goal completes. The pattern differs from generative AI by the loop, the tool calls, and the autonomy. The control-plane implications are distinct: identity at the agent level, scoped permissions for each tool call, audit records for each step in the loop, and the question of who carries liability for the agent decisions. The NIST AI agent identity and authorization framework took comments through April 2, 2026 and set the operational baseline.

agentic-aiai-agentsai-governanceauditinline-enforcementcompliance
Read post →

AI Agent Privilege Abuse: Why Service Credentials Become Effective Superuser Accounts in Multi-Step Agent Workflows

A typical AI agent runs on a single service credential that combines the permissions of every action the agent might need to take. The credential is the union, not the intersection. An agent decomposing a goal can take any action the credential authorizes, including actions the user never intended to delegate. The post-authentication gap is the difference between "the agent is authenticated" and "this specific action against this specific resource is permitted by the user." Closing the gap requires identity propagation from the user through the agent to each tool call.

agentic-aiai-agentsidentityauthorizationai-securityaudit
Read post →

Shadow AI Detection: The Three Signals That Actually Identify Unauthorized LLM Use Inside the Enterprise

Shadow AI detection works on three signals: DNS resolution to known LLM endpoints, HTTP request shape against published API contracts, and identity-bound prompt content captured at the HTTP layer. Network DLP and CASB inventories miss the prompt body because it sits inside TLS to a sanctioned destination. This piece walks through each signal, what the detection misses without inline inspection, and the architectural pattern that produces a per-request record auditors can sample.

shadow-aishadow-ai-detectionai-securityinline-enforcementvisibility
Read post →

AI Agent Action Lineage: Reconstructing What an Autonomous Agent Did From the Audit Record

AI agent action lineage is the record series that lets a security team reconstruct what an autonomous agent did across a sequence of LLM calls, tool invocations, and downstream actions. The record has to carry the agent identity, the originating user identity, the prompt and response on every step, the policy state, and the cross-references between steps. This piece walks through the lineage record, where it sits, and what audit obligations it satisfies.

ai-agent-action-lineageagentic-aiai-agent-securityaudit-logseu-ai-act
Read post →

The AI Agent Post-Authentication Gap: Why Identity at Login Is Not Identity at the Tool Call

Most enterprise agent architectures authenticate the user at the start of the session and then let the agent run with a service identity that carries no user context. The gap between the login identity and the per-tool-call identity is the post-authentication gap. This piece walks through the gap, where it shows up in production, the audit record fields it breaks, and the architectural pattern that closes it.

ai-agent-post-authentication-gapagentic-aiai-agent-securityai-agent-identityaudit-logs
Read post →

Employee Copilot Usage Policy: What to Cover and How to Enforce It

Microsoft Copilot, GitHub Copilot, and the family of Copilot products sit inside enterprise workflows where employees handle confidential information by default. The policy that governs how employees use Copilot has to cover data classification, prompt content rules, output handling, attribution, and audit. This piece walks through the seven sections every Copilot usage policy needs, the enforcement layer the policy depends on, and the common mistakes that produce policies the security team cannot demonstrate compliance with.

copilotai-usage-policyshadow-aigovernancepolicy-template
Read post →