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AI Security Solutions

97 posts on ai security solutions.

Claude Connectors DLP: Where Data Loss Prevention Sits for Remote MCP Servers

Claude Connectors are built on the Model Context Protocol, and the transport a connector uses determines whether data loss prevention has anywhere to stand. Remote connectors speak HTTP to a server the organization can put a proxy in front of. Local connectors speak STDIO between two processes on the same machine, where no network control exists. This piece separates the two transports, walks the tool-result path that carries the real exfiltration risk, and sets out what a per-decision record needs to contain.

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AWS Bedrock DLP: Classifying Prompt Content Before It Reaches the Inference Path

IAM decides which principal may invoke which Bedrock model, and says nothing about what is inside the prompt. Bedrock Guardrails evaluate content inside the AWS inference path and cover AWS-hosted endpoints. This piece walks the bedrock-runtime request path, explains why model invocation logging being off by default is the most consequential setting in the service, separates the AWS-native controls from the multi-provider case, and sets out what a per-decision record has to carry.

ai-securityllm-securitydata-loss-preventiondlpcloud-securitypolicy-enforcement
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Azure AI Foundry DLP: Content Filters Answer a Different Question Than Data Classification

Azure AI Foundry content filters evaluate harm categories and jailbreak attempts. Data loss prevention asks whether the prompt contains customer records that should never have left the tenant. Those are different questions with different classifiers, and conflating them is the most common gap in a Foundry security review. This piece walks the per-deployment endpoint surface, the diagnostic-settings problem across a multi-model project, and where prompt classification and per-request authorization belong.

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Amazon Q DLP: The Index, the ACL Window, and What an Enforcement Layer Can Reach

Amazon Q Business builds an index over enterprise data sources and answers with the acting user''s permissions applied. Two data-protection problems survive that design: the index is a second copy of the content under different controls, and permission changes at the source only reach the index at the next sync. This piece separates the managed surfaces an enforcement layer cannot intercept from the Q Developer and custom-plugin paths it can, and sets out what to test.

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Cohere DLP: The Embedding Pipeline Moves More Data Than the Chat Endpoint

A Cohere deployment usually moves far more content through Embed and Rerank than through Chat, and most AI DLP programmes instrument only the conversational path. An indexing job embedding a 40,000-document corpus sends every one of those documents to the provider in a batch that no chat dashboard records. This piece walks the three endpoint families, explains why deployment-mode variation makes hostname allowlisting fragile, and sets out where classification and per-decision records belong.

ai-securityllm-securitydata-loss-preventiondlparchitecturepolicy-enforcement
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Cursor DLP: Four Paths Send Your Codebase to a Model, and Autocomplete Is the Loudest

A developer using Cursor produces model requests from four distinct paths: codebase indexing, tab completion, chat with context, and agent mode. Tab completion alone fires on a keystroke cadence and sends surrounding file content thousands of times a day, which is a volume profile no chat-oriented AI DLP programme was built for. This piece separates the four paths, explains why endpoint DLP and network allowlisting both miss them, and sets out where classification and per-decision records belong.

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CrewAI DLP: The Delegation Chain Moves Data That No Single Agent Ever Saw

A CrewAI crew with four agents and a hierarchical process produces far more model calls than the task list suggests, because delegation, shared memory and tool output all become context on the next call. Instrumenting the first call and treating the rest as internal traffic misses most of the content leaving the boundary. This piece walks the four data paths inside a crew, explains why the manager agent concentrates the risk, and sets out where classification and per-decision records belong.

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DeepSeek DLP: One Model Name, Four Deployment Paths, Four Different Answers About Where Data Goes

DeepSeek models reach an enterprise through at least four paths: the vendor API, self-hosted open weights, third-party inference hosts, and consumer apps on unmanaged devices. Each path has a different data destination, a different jurisdiction and a different retention posture, while all four produce requests that look alike to an application. This piece separates the four, explains why a policy written against a model name governs nothing, and sets out where classification and per-decision records belong.

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Databricks Mosaic AI DLP: Unity Catalog Governs the Table, Not the Prompt Built From It

Unity Catalog governs which principal can read which table, column and row inside the lakehouse. A Mosaic AI application reads a governed table, assembles a prompt from the rows, and sends that prompt to a model endpoint, at which point the governance metadata stops travelling with the content. This piece walks the four Mosaic AI paths that cross the model boundary, explains why external model serving changes the risk profile, and sets out where classification and per-decision records belong.

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Google Agentspace DLP: A No-Code Agent Builder Puts Data-Movement Design in Non-Engineer Hands

Google Agentspace lets a business user assemble an agent from connectors, a model and a set of actions without writing code. That moves the decision about which data reaches a model from an engineering review into a self-service form. This piece walks the connector, action and agent-build paths, explains why agent proliferation is the governance problem rather than any single agent, and sets out where classification and per-decision records belong.

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Glean DLP: The Knowledge Graph Holds Content Nobody Ever Wrote Down

Glean indexes documents and also builds a knowledge graph over people, teams, projects and activity signals. That derived layer contains inferences no document states, and those inferences become retrieval context in prompts. Add Glean Agents taking actions across connected systems and the surface widens again. This piece separates the document index from the derived graph, explains why the agent action path needs different controls from the answer path, and sets out where classification and per-decision records belong.

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Dropbox Dash DLP: Connectors Turn Twelve Separate Permission Models Into One Prompt

Dropbox Dash indexes content across connected sources and answers questions over the combined result. The security property that matters is the fan-in: content from a dozen systems, each with its own permission model and its own history of over-sharing, converges into a single retrieval corpus and a single prompt. This piece walks the connector paths, explains why inherited over-permissioning surfaces at retrieval time, and sets out where classification and per-decision records belong.

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