97 posts on ai security solutions.
OpenAI Agent Builder can carry enterprise context into an LLM workflow. DLP needs a decision point that evaluates the prompt, response, originating identity, data classification, and selected route before content reaches the model. This article sets out the request-path evidence a security review should require.
NVIDIA NIM can carry enterprise context into an LLM workflow. DLP needs a decision point that evaluates the prompt, response, originating identity, data classification, and selected route before content reaches the model. This article sets out the request-path evidence a security review should require.
Mistral can carry enterprise context into an LLM workflow. DLP needs a decision point that evaluates the prompt, response, originating identity, data classification, and selected route before content reaches the model. This article sets out the request-path evidence a security review should require.
Microsoft 365 Copilot can carry enterprise context into an LLM workflow. DLP needs a decision point that evaluates the prompt, response, originating identity, data classification, and selected route before content reaches the model. This article sets out the request-path evidence a security review should require.
LlamaIndex can carry enterprise context into an LLM workflow. DLP needs a decision point that evaluates the prompt, response, originating identity, data classification, and selected route before content reaches the model. This article sets out the request-path evidence a security review should require.
Ten LLM observability platforms grouped by what they were built for: framework-native tracing, agent session replay, evaluation-first workflows, and infrastructure telemetry. Each entry covers the architecture, the instrumentation model, and the buyer it fits. The final sections cover the two structural limits every tool in this category shares, sampling and application-controlled writes, and what that means for compliance evidence.
LLM observability is the practice of capturing enough detail about model calls to reconstruct why a specific request behaved the way it did. This covers the four signals it collects, the three instrumentation models that determine coverage, a worked trace, the questions it answers well, and the two categories of question it structurally cannot answer.
An AI data security program has to account for five data classes that leave an org through AI traffic: prompts, uploaded files, RAG context, tool-call arguments, and model responses. This article maps where each is governable on the decrypted HTTP request path, gives a data-class policy rule and a per-decision audit record auditors can read, and is honest about the three data problems a policy gateway does not solve.
AI threat detection means two different things. This article is about detecting threats inside AI traffic itself: prompt injection attempts, an unrecognized identity making model calls, anomalous provider destinations, and exfiltration patterns in responses. It shows why those signals are only observable on the decrypted HTTP request path, gives a per-decision audit-log event shape and an anomaly rule, and is honest that a gateway is not an EDR or SIEM and instead feeds them.
A deployed model exposes one runtime interface an attacker can reach: the inference endpoint. This article maps the access-boundary attack surface for enterprise deployers, separates it from model-layer disciplines like training-data poisoning and weight tampering, and shows the three controls that actually sit on the request path: identity-aware authorization, inbound and outbound policy, and a per-decision audit record.
AI access control decides which human user or agent identity may call which model, with which scopes, under which conditions. This article contrasts the shared-API-key model that grants god-mode access with identity-aware, per-request authorization at the gateway, gives a working policy example, and marks the boundary against cloud IAM and data-store ACLs.
AI cloud security diverges from generic cloud security at one surface: the outbound HTTPS path from your workloads to managed model endpoints like bedrock-runtime.us-east-1.amazonaws.com, {resource}.openai.azure.com, and us-central1-aiplatform.googleapis.com. This article maps where CSPM, IAM, VPC config, and KMS stop, and shows how an identity-aware gateway governs which cloud identity may reach which model endpoint and records each call.