Public-source research
Review what vendors say publicly, the exact quoted source text, the security requirement each statement may support, and what still needs verification.
Showing 281–300 of 1280 evidence records
ModelOp claims portfolio-level dashboards and reporting for AI cost, value, return on investment (ROI), and use-case cost tracking.
Executive dashboards tracking AI value, cost, and risk over time
Runlayer claims centralized AI cost monitoring, spend attribution to teams and workflows, and budget-conditioned access for users and agents.
Monitor AI usage, cost, adoption, and agent activity in one place, then tie spend back to the teams and workflows getting real value.
AWS Bedrock native-control materials reviewed did not provide a public claim for discovering employee use of third-party AI apps.
AWS Bedrock native-control materials reviewed did not provide a public claim for software as a service (SaaS) AI inventory or embedded third-party software as a service (SaaS) AI discovery.
AWS claims Bedrock Guardrails can block harmful content and use Automated Reasoning checks to reduce hallucinations and data ambiguity.
Bedrock Guardrails can help block up to 88% of harmful content and identify correct model responses with up to 99% accuracy to minimize hallucinations and data ambiguity using Automated Reasoning checks.
AWS claims Bedrock does not store or use customer data to train models and supports encryption and identity-based access policies.
Bedrock never stores or uses your data to train models, ensuring complete security and privacy, with encryption of data in transit and at rest, as well as identity-based policies for managing data access.
AWS claims Bedrock AgentCore includes tracing, debugging, and evaluation capabilities for agent performance.
continuously optimize agent performance with tracing, debugging, and evaluation capabilities built in.
AWS claims Bedrock AgentCore connects agents to enterprise systems, tools, and data with automatic authentication and access controls.
connect agents to enterprise systems, tools, and data with automatic authentication and access controls
AWS Bedrock native-control materials reviewed did not provide a public Model Context Protocol (MCP), agent-to-agent (A2A), or agent-to-agent communication security claim.
AWS claims Bedrock cost-optimization features such as Model Distillation, Prompt caching, and Intelligent Prompt Routing can reduce expenses.
Features like Model Distillation, Prompt caching, and Intelligent Prompt Routing can reduce expenses while maintaining performance.
Google Model Armor native-control materials reviewed did not provide a public claim for discovering employee use of third-party AI apps.
Google Model Armor native-control materials reviewed did not provide a public claim for software as a service (SaaS) AI inventory or embedded third-party software as a service (SaaS) AI discovery.
Google claims Model Armor filters prompts and responses to protect large language models (LLMs) from malicious or sensitive content exposure or generation.
Model Armor filters both input (prompts) and output (responses) to prevent the LLM from exposure to or generation of malicious or sensitive content.
Google claims Model Armor can mitigate leakage of sensitive IP and PII in large language model (LLM) prompts or responses.
Mitigate the risk of leaking sensitive intellectual property (IP) and personally identifiable information (PII) in LLM prompts or responses.
Google claims Gemini Enterprise Agent Platform can call Model Armor to inspect or block traffic that violates defined policies.
Gemini Enterprise Agent Platform calls the Model Armor service, which inspects or blocks traffic that violates your defined policies
Google claims Cloud Logging can show Model Armor sanitization results for prompts and responses in Gemini Enterprise Agent Platform integration.
You need to enable Cloud Logging to view the sanitization results of prompts and responses.
Google claims Model Armor can intercept prompts and responses for Gemini Enterprise Agent Platform traffic.
Model Armor intercepts prompts before they reach Gemini models, and intercepts responses before your application receives them.
Google says Model Armor uses total prompt and response tokens for pricing and limits tokens processed in each prompt and response.
Model Armor uses the total number of tokens in AI prompts and responses for pricing purposes. Model Armor limits the number of tokens processed in each prompt and response.
Microsoft Foundry and Agent ID native-control materials reviewed did not provide a public claim for discovering employee use of third-party AI apps.
Microsoft Foundry and Agent ID native-control materials reviewed did not provide a public claim for software as a service (SaaS) AI inventory or embedded third-party software as a service (SaaS) AI discovery.