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Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.

LLM Security Monitoring: Five Vantage Points, and What Each One Can Physically See

An LLM security monitoring program is decided by where the telemetry is collected, because each vantage point has a physical limit on what it can observe. DNS and proxy logs see domains. CASB sees applications. Provider dashboards see usage without corporate identity. Application logs see everything and are written by the system under investigation. This compares all five, states the fidelity limit of each, and gives the event schema worth sending to a SIEM.

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The LLM Security Checklist: 34 Items With Pass Criteria You Can Actually Fail

Most LLM security checklists list topics. A checklist that changes a deployment decision states a pass criterion specific enough to fail. This one runs 34 items across seven sections: inventory, identity, prompt controls, response handling, tools and agents, evidence, and supply chain. Each item has a criterion, and the three that fail most often in real reviews are called out with the reason they fail.

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LLM Jailbreak Detection: The Four Signal Families and the Base-Rate Problem Nobody Budgets For

Jailbreak detection is a classification problem running against a hostile base rate. Four signal families are available: lexical patterns, semantic classifiers, response-side refusal analysis, and multi-turn escalation. Three of them are evaluated before the model responds and one after. This walks each family, the false-positive economics that decide whether the detector survives contact with production, and the record a detection has to write to be worth anything in an investigation.

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LLM Cyber Security: Four Attacker Objectives and the Control Point That Answers Each One

Most LLM security guides organize around defense layers. Attackers organize around objectives: exfiltrate data, gain execution, abuse spend, corrupt output. This guide takes each objective in turn, traces the path it uses through an enterprise LLM deployment, names the 2026 CVEs that made each path real, and identifies which control point answers it. It also states plainly which objectives the AI request boundary cannot reach.

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LLM Application Security: The Five Layers and Which One Holds When the Others Fail

Securing an LLM application means securing five layers: the model, the prompt, the retrieval pipeline, the tool surface, and the request boundary. Four of them are inside the application's own process, which means an attacker who reaches the process owns those controls. This walks each layer, the specific attacks it faces, and why the request boundary is the only layer that produces enforceable decisions and independent evidence.

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Data Exfiltration via LLM: The Channel Your DLP Was Never Built to See

An LLM prompt is an HTTPS POST to a provider API carrying whatever the user or agent put in the context window. That makes it an egress channel with no size limit, no content inspection, and no identity correlation in most enterprises. This walks the four exfiltration paths through an LLM (deliberate paste, agent retrieval, injected instruction, response-side leakage), what each looks like on the wire, and the control that closes them.

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AI Model Poisoning: Why the Right Response Is to Stop Trusting the Model

Model poisoning covers three distinct attacks: corrupting pretraining data, planting a backdoor during fine-tuning, and poisoning the retrieval index an application queries at inference. The first two are largely undetectable from the deploying enterprise's position. That fact should reshape the control strategy rather than the detection strategy. This walks the taxonomy, states plainly which layers an enterprise can and cannot inspect, and makes the case for treating model output as untrusted input.

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Agentic AI vs AI Agents: The Distinction That Changes Your Threat Model

An AI agent is a component: a model with tools and a loop. Agentic AI is a property of a system: the degree to which it selects its own next action from state it produced. The terms get used as synonyms, and treating them that way collapses a distinction that decides which controls you need. This walks the autonomy gradient, where each level's threat model changes, and the one control point that holds across all of them.

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Agentic AI Use Cases and the Security Profile Each One Carries

Agentic AI use cases get evaluated on business value and deployed on that basis, and the security profile is discovered afterwards. The profile is predictable from two variables: what data the agent can reach and what side effects it can cause. This walks six enterprise agent use cases (customer support, coding, procurement, IT operations, clinical documentation, financial reconciliation), the specific failure each carries, and the control that bounds it.

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Agentic AI Runtime Security: The Controls That Only Exist While the Agent Is Running

An agent's threat model is decided at runtime, because its next action is chosen from content it retrieved a moment ago. Design-time review cannot see that. This walks the four runtime control points in an agent loop (plan, tool call, context assembly, model call), what each one can enforce, and why the model call is the only point where an external enforcement layer can render a deterministic decision on every step.

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Agentic AI Audit Trail: Reconstructing a Multi-Step Agent Run From the Record

An agent run is not one request. It is a chain: a plan, a sequence of tool calls, retrieved context, and model calls whose inputs were produced by earlier steps in the same chain. An audit trail that records only the final model call cannot reconstruct any of it. This walks the fields an agentic audit trail has to carry, the correlation identifier that makes a chain reconstructible, and why the record has to be written outside the agent that produced it.

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Secure AI Gateway: The Seven Properties That Decide Whether It Is a Control

Most products described as a secure AI gateway are routing layers with a content filter attached. A gateway becomes a security control when it holds seven properties: identity binding, per-request authorization, fail-closed behavior, prompt-level data classification, model-agnostic reach, write-path independence for its audit records, and a bounded latency budget. I walk each property, the failure mode it removes, and the question to ask a vendor that claims it.

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