Best AI Usage Control Software for Microsoft Azure

Compare the Top AI Usage Control Software that integrates with Microsoft Azure as of May 2026

This a list of AI Usage Control software that integrates with Microsoft Azure. Use the filters on the left to add additional filters for products that have integrations with Microsoft Azure. View the products that work with Microsoft Azure in the table below.

What is AI Usage Control Software for Microsoft Azure?

AI usage control software helps organizations monitor, govern, and limit how artificial intelligence systems are accessed and used across teams and applications. It provides visibility into AI consumption patterns, tracks model usage, and enforces policies such as access rights, quotas, and cost thresholds to prevent misuse or overuse. The software often includes real-time alerts, audit logs, and compliance reporting to ensure responsible AI deployment and alignment with internal governance standards. Many solutions integrate with identity management, billing systems, and AI platforms to centralize control and policy enforcement. By managing how AI is used across an organization, AI usage control software enhances security, cost efficiency, and ethical compliance. Compare and read user reviews of the best AI Usage Control software for Microsoft Azure currently available using the table below. This list is updated regularly.

  • 1
    Airia

    Airia

    Airia

    Airia’s enterprise AI orchestration platform seamlessly integrates with existing systems and data sources, offering a no-code agent builder for rapid prototyping, pre-built connectors for unified data integration, intelligent AI operations that optimize performance and costs through smart routing and centralized lifecycle management, and enterprise-grade security and governance with detailed audit capabilities and responsible AI guardrails. Model-agnostic and vendor-neutral, it supports flexible deployment across shared or dedicated cloud, private cloud, and on-premises environments, enabling both technical and business users to build, deploy, and manage secure AI agents at scale without complex installation or migration. Its intuitive interface and unified platform transform workflows across functions, from engineering and IT to finance, legal, marketing, sales, and support, so organizations can accelerate AI initiatives with confidence and compliance.
    Starting Price: $49 per month
  • 2
    Nightfall

    Nightfall

    Nightfall AI

    Discover, classify, and protect your sensitive data. Nightfall™ uses machine learning to identify business-critical data, like customer PII, across your SaaS, APIs, and data infrastructure, so you can manage & protect it. Integrate in minutes with cloud services via APIs to monitor data without agents. Machine learning classifies your sensitive data & PII with high accuracy, so nothing gets missed. Setup automated workflows for quarantines, deletions, alerts, and more - saving you time and keeping your business safe. Nightfall integrates directly with all your SaaS, APIs, and data infrastructure. Start building with Nightfall’s APIs for sensitive data classification & protection for free. Via REST API, programmatically get structured results from Nightfall’s deep learning-based detectors for things like credit card numbers, API keys, and more. Integrate with just a few lines of code. Seamlessly add data classification to your applications & workflows using Nightfall's REST API.
  • 3
    Prompt Security

    Prompt Security

    SentinelOne

    Prompt Security enables enterprises to benefit from the adoption of Generative AI while protecting from the full range of risks to their applications, employees and customers. At every touchpoint of Generative AI in an organization — from AI tools used by employees to GenAI integrations in customer-facing products — Prompt inspects each prompt and model response to prevent the exposure of sensitive data, block harmful content, and secure against GenAI-specific attacks. The solution also provides leadership of enterprises with complete visibility and governance over the AI tools used within their organization.
  • 4
    CyberTide

    CyberTide

    CyberTide

    CyberTide is an AI-native data security platform designed to give organizations full visibility, control, and protection over sensitive data across cloud, SaaS, collaboration tools, and generative AI environments. It combines multiple security capabilities into a unified stack, including Data Loss Prevention (DLP), Data Security Posture Management (DSPM), insider risk management, and AI security posture management, allowing teams to detect, classify, and secure data in real time. It uses context-aware artificial intelligence to analyze the meaning and relationships of data rather than relying on keywords, significantly reducing false positives while achieving high-precision detection of sensitive information. It continuously scans data at rest and in motion, across emails, chats, files, and AI prompts, enforcing policies that prevent unauthorized sharing, leakage, or misuse of confidential data such as personal, financial, or proprietary information.
  • 5
    FireTail

    FireTail

    FireTail

    FireTail is an end-to-end AI security and governance platform designed to give organizations complete visibility, control, and protection over how artificial intelligence is used across their environments. It continuously discovers AI usage across code, cloud infrastructure, APIs, SaaS tools, and browsers, building a real-time inventory of both approved and shadow AI systems to ensure nothing operates outside governance. It captures and analyzes every AI interaction, including prompts, responses, metadata, and user identity, providing deep contextual visibility into how AI models are accessed and how data flows through them. FireTail enables organizations to enforce flexible, context-aware policies through a centralized governance engine, using prebuilt frameworks such as OWASP or custom rules to maintain compliance without slowing innovation. It continuously monitors activity to detect risks like prompt injection, data leakage, model misuse, and anomalous behavior.
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