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Results for “Ai Systems”
26 publicationsCount tokens before sending a prompt to Amazon Bedrock
Count a Bedrock prompt for the chosen model, check endpoint support, distinguish context and output limits, and verify usage without logging prompts.
AI systems · AWS · By Cloud Security DeskRemove a document from an Amazon Bedrock knowledge base
Retire an S3 document from a Bedrock knowledge base through source changes, sync results, retrieval checks, and review of older application copies.
AI systems · AWS · By Cloud Security DeskCall Vertex AI from Cloud Run without a service account key
Use a Cloud Run service account and ADC to call Vertex AI, separate deployment and runtime permissions, and remove an obsolete prototype key.
AI systems · Google Cloud · By Cloud Security DeskRemove unnecessary personal data before sending Vertex AI prompts
Reduce personal data in Vertex AI prompts with field selection, Sensitive Data Protection, deliberate transformations, and safe failure handling.
AI systems · Google Cloud · By Cloud Security DeskHandle Azure OpenAI rate limits without retry storms
Separate deployment quota, short request bursts and bounded client retry behavior.
AI systems · Azure · By Cloud Security DeskConnect to Azure OpenAI without an API key
Prove Entra-based inference with a scoped runtime identity before disabling local authentication.
AI systems · Azure · By Cloud Security DeskBind confidential GPU inference to a verified key release
Require composite evidence, owner policy and verified recipient binding before confidential inference receives protected key material.
AI systems · NVIDIA / Azure · By Cloud Security DeskChoose who can share an inference prefix cache
Choose the principals allowed to share prefix state, then carry that decision through request routing, offload, transfer and restore.
AI systems · vLLM / NVIDIA · By Cloud Security DeskEmbeddings still need a sensitive data boundary
Treat vector access as a derived-data release decision, with separate permissions for retrieval, debugging and bulk export.
AI systems · By Cloud Security DeskGive persistent agent memory an admission boundary
Separate conversational candidates from admitted memory, preserve their permitted audience, and make derived summaries repairable.
AI systems · LangChain · By Cloud Security DeskIsolate document parsing before RAG ingestion
Give document parsing a bounded worker, then admit its extracted content separately before embedding or indexing.
AI systems · Apache Tika / Kubernetes / gVisor · By Cloud Security DeskDecide what happens when an AI guardrail fails
Distinguish denial, incomplete checks and late results, then decide what may cross each protected release point.
AI systems · Algolia / TrueFoundry / Conductor · By Cloud Security DeskSecurity evidence for AI release decisions
A release approval should identify the changed application, the claims its tests support and the evidence that expires when a model, prompt, data path or runtime changes.
AI systems · By Cloud Security DeskWhat coding benchmarks can prove about a model
A coding benchmark result depends on its tasks, harness and tests. A reproducible count of SWE-bench Verified shows why the denominator belongs beside every comparison.
AI systems · By Cloud Security DeskWhere fine tuning data needs a trust boundary
A training dataset can preserve its checksum and still teach the wrong behavior. Admission controls need to separate origin, transformation, approved use and the model change they produce.
AI systems · By Cloud Security DeskA budget model for bounded AI inference
Request throttles, token quotas and billing alerts control different things. An inference service needs an admission decision that reserves bounded work and reconciles what actually ran.
AI systems · AWS / Kubernetes / vLLM · By Cloud Security DeskAn evidence plan for AI incident response
An AI incident record needs to connect the system version, context, output, downstream action and observed consequence. A saved prompt or a public report is only part of that evidence.
AI systems · By Cloud Security DeskModel output needs its own trust boundary
A model can produce valid JSON containing an unauthorized identifier, an unsafe link or text that a renderer interprets as code. The application consuming that output owns the next trust decision.
AI systems · OpenAI · By Cloud Security DeskMap every connection beyond the private AI endpoint
Trace inference, retrieval, tools, administration, and telemetry separately before describing an AI application as private.
AI systems · AWS · By Cloud Security DeskKeep sensitive prompts out of routine telemetry
Collect diagnostic metadata for a defined purpose and treat prompts, retrieved documents, tool arguments, and responses as a separate content-capture decision.
AI systems · AWS · By Cloud Security DeskPut enforceable boundaries around agent tool calls
Treat model proposals as requests for authority, then check the operation, resource, recipient, and approval at the point where a tool can create a side effect.
AI systems · AWS · By Cloud Security DeskTreat model downloads as software supply chain changes
Review weights, custom code, dependencies, and runtime authority as separate decisions before promoting a downloaded model into a trusted environment.
AI systems · Hugging Face · By Cloud Security DeskKeep MCP tokens bound to the intended resource
Keep token audiences, user consent, and downstream tool authority separate when reviewing a protected HTTP MCP service.
AI systems · MCP · By Cloud Security DeskKeep private documents out of shared RAG answers
Authorize retrieved documents before they enter model context, preserve permissions on chunks, and make source access changes visible in the retrieval path.
AI systems · Azure · By Cloud Security Desk