97 posts on ai security solutions.
Llama Enterprise can sit inside a valuable enterprise AI workflow, but DLP requires a decision point that sees the prompt, response, originating identity, and route before content reaches the model. This article defines that enforcement boundary and the evidence security teams need.
LangChain can sit inside a valuable enterprise AI workflow, but DLP requires a decision point that sees the prompt, response, originating identity, and route before content reaches the model. This article defines that enforcement boundary and the evidence security teams need.
Intercom Fin can sit inside a valuable enterprise AI workflow, but DLP requires a decision point that sees the prompt, response, originating identity, and route before content reaches the model. This article defines that enforcement boundary and the evidence security teams need.
IBM watsonx can sit inside a valuable enterprise AI workflow, but DLP requires a decision point that sees the prompt, response, originating identity, and route before content reaches the model. This article defines that enforcement boundary and the evidence security teams need.
Hugging Face can sit inside a valuable enterprise AI workflow, but DLP requires a decision point that sees the prompt, response, originating identity, and route before content reaches the model. This article defines that enforcement boundary and the evidence security teams need.
HubSpot Breeze can sit inside a valuable enterprise AI workflow, but DLP requires a decision point that sees the prompt, response, originating identity, and route before content reaches the model. This article defines that enforcement boundary and the evidence security teams need.
Grok Enterprise can sit inside a valuable enterprise AI workflow, but DLP requires a decision point that sees the prompt, response, originating identity, and route before content reaches the model. This article defines that enforcement boundary and the evidence security teams need.
Data residency for AI breaks the moment a prompt leaves your region for a model endpoint in another one, and application code rarely records where the call went. This piece shows how an AI gateway turns residency from a hopeful configuration into an enforced, logged control: identity and data class decide which regional endpoint a call may reach, and every decision is recorded with its destination.
Non-human identities, service accounts, API keys, workloads, and now AI agents, outnumber humans by more than 100 to 1 in the average enterprise (Palo Alto Networks, 2026). They carry static long-lived credentials, skip MFA, and share keys, which means the audit trail names a credential rather than an actor. This piece explains what an NHI is, why it resists the controls built for people, the specific attribution problem AI agents add on a shared key, and where identity-bound authorization on each AI call fits.
Vercel AI Gateway gives a team one OpenAI-compatible endpoint in front of hundreds of models, with a single API key, automatic provider failover, spend limits, and per-request observability. Those features route and meter the traffic. They stop at the question of whether the authenticated caller behind a request is permitted to send this particular prompt, carrying this data classification, to this model. This piece walks the controls Vercel ships, marks where they stop, and shows the identity-aware authorization layer that closes the post-authentication gap on AI gateway traffic.
Databricks Mosaic AI Gateway puts rate limiting, usage tracking, payload logging, and PII guardrails in front of the model serving endpoints inside a Databricks workspace. Those controls govern traffic to endpoints Databricks fronts. They stop at the question of whether a specific authenticated caller is permitted to send a specific prompt to a model, and at any traffic that leaves the workspace to a non-Databricks LLM. This piece walks the controls Databricks ships, marks where they stop, and shows the identity-aware authorization layer that closes the gap on AI request traffic.
AutoGen agents pass messages to each other, and a shared conversation history means one sensitive document read once is re-sent to the model on every subsequent turn by every participating agent. This piece walks the message-passing mechanism that multiplies disclosure, separates the local code-execution surface from the model-client surface an enforcement layer can reach, shows where a base URL points a deployment at a policy boundary, and sets out what the per-decision record needs to carry.