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Problem-Aware

88 posts on problem-aware.

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.

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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.

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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.

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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.

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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.

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RAG Prompt Injection: How the Retrieval Step Becomes the Attack Surface

RAG prompt injection turns the retrieval step into the attack surface. Adversarial content inside a retrieved document reaches the model context with the same trust level as the application instructions. The model has no architectural way to distinguish trusted spans from untrusted spans. This piece walks through the four retrieval paths that open the surface, the failure modes the model alone cannot close, and the inspection-layer controls that produce a deterministic decision and an audit record EU AI Act Article 12 reviewers will accept.

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Prompt Injection vs Jailbreak: Where the Two Attack Classes Diverge and What the Inspection Layer Enforces

Prompt injection and jailbreaking are distinct attack classes that public discussion often conflates. Jailbreaking targets the model provider safety training to produce content the provider intended to suppress. Prompt injection targets the application context boundary to override the application instructions or exfiltrate organization data. The defenses sit at different architectural layers. This piece walks through the distinction, where each defense layer fires, and the inspection-layer pattern that addresses both.

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Prompt Injection Test Cases: The Twelve Patterns Your Red Team Has To Run

Prompt injection test cases for production AI deployments cluster into twelve patterns the red team has to exercise: instruction-override, role-reversal, encoded payloads, indirect injection through retrieved content, tool-output injection, multi-turn persuasion, authority impersonation, output-formatting hijack, translation pivot, long-context dilution, system-prompt extraction, and authorization-bypass. This piece walks through each pattern, the payload structure, the expected inspection-layer verdict, and the audit record the test should produce.

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Prompt Injection Examples: 12 Real Patterns From Production Incidents and the Inspection Layer Response

Prompt injection examples that surface in production AI systems follow a small number of repeatable patterns. The patterns appear across customer support agents, RAG pipelines, agentic browsers, and code-assist tools. Each pattern has a control point at the request boundary where an inspection layer can produce a deterministic signal the policy can act on. This piece walks through twelve patterns from production incident response, the injection text that triggers each, the inspection-layer response that holds up, and the audit record that supports the post-incident review.

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AI Agent Supply Chain Attacks: How the Request Boundary Becomes the Failing Surface

AI agent supply chain attacks compromise the agent at one of three points: the model artifact, the tool the agent calls, or the runtime input the agent processes. The HTTP request boundary between the authenticated agent and the LLM endpoint sits underneath all three failure modes. This piece walks through the attack patterns reported in 2025 and 2026, the architectural defects that enable each one, and the inspection-layer control that closes the runtime side of the supply chain risk.

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Prompt Injection Mitigation Techniques: The Eight Controls That Hold Up Under Review

Prompt injection mitigation in production AI deployments splits into eight controls: prompt structure, input classifiers, retrieval-time content evaluation, identity-bound policy enforcement, output classifiers, tool call authorization, conversation-aware state checks, and per-decision audit records. This piece walks through what each control catches, what each one misses, and the architectural layer where each fires. The pattern that holds up under EU AI Act Article 12 and DORA Article 19 review.

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Prompt Injection Attack Examples: Ten Production Payloads and the Request-Boundary Response

Prompt injection attack examples in production AI systems cluster into ten repeatable payload families. Each one targets a specific gap between the application instructions and the model context window. This piece walks through the payload, the failure mode the attacker exploits, and the request-boundary response that produces a deterministic block decision and an audit record an EU AI Act Article 12 or DORA Article 19 reviewer will accept.

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