As enterprises accelerate AI adoption, large language models (LLMs) hosted on public cloud platforms are quickly becoming the norm due to their simplified access and pricing model. Cloud-native services like AWS Bedrock, Azure AI Foundry, and Google Vertex AI offer powerful, pay-as-you-go access to Foundational AI Models and enterprise-grade pre-trained marketplace LLMs with just a few clicks.
But ease of use brings exposure. Without proper visibility of cloud-hosted AI services and models, organizations risk blind spots, misconfigurations, and ungoverned AI behavior. That’s why proactive discovery and risk management for AI are no longer optional; they are a new security imperative.
The Cloud AI Shift Is Here, But So Are New Risks
Cloud-native AI platforms allow rapid model deployment, but they also introduce new security challenges. Security teams need visibility into LLMs and AI workloads being used, their access paths, configuration states, and connections to sensitive data.
Qualys TotalAI helps enterprises inventory and monitor cloud-based AI services and LLMs, enabling security teams to get ahead of the risk curve.
Your LLM Inventory is Security Ground Zero
As with any security posture management, AI security also starts with complete visibility. However, the dynamic nature of AI and the emerging attack vectors it introduces can obscure visibility, leading to blind spots.
Security teams need end-to-end visibility into AI usage across the organization, the ability to proactively detect AI Risks such as:
- Shadow AI: Unauthorized AI model usage
- Sensitive training data exposure
- Model access misuse
- IP theft and regulatory violations
Qualys TotalAI addresses this with AI fingerprinting capabilities that extend to cloud native AI services. Learn more about how TotalAI provides AI-specific discovery in this deep dive on LLM security.
With this unique coverage, organizations gain a comprehensive inventory view of:
- Cloud-based LLMs
- Model versions and runtime configurations
- Associations with data pipelines, services, and cloud resources.
This visibility empowers teams to detect misconfigurations, identify vulnerable AI assets, and essentially safeguard AI workloads in the cloud.
Learn how Qualys began this journey in our early perspective on de-risking generative AI, where we laid the foundation for TotalAI and what we’ve learned since.
CSPM Gives You Cloud Visibility, TotalAI Adds AI Risk Context
Traditional cloud security tools like CSPM focus primarily on infrastructure, ensuring best practices, detecting misconfigurations, and enforcing compliance. This remains critical for minimizing baseline risk in cloud workloads.
Qualys TotalCloud extends traditional CSPM by offering agentless discovery, real-time policy enforcement, and risk-based visibility across multi-cloud environments. It provides a strong foundation for securing cloud infrastructure, identities, data flows, and configurations, especially in dynamic DevOps-driven ecosystems.
As AI workloads increasingly run on this same infrastructure, they introduce an entirely new layer of complexity to the risk landscape. Risks like model manipulation, prompt injection, IP theft, data leakage, and privilege escalation require deeper, AI-specific inspection. These risks demand a fundamentally new approach to securing AI behavior, data, and supply chains, built on the deeper understanding of the behavioral risks and operational footprint of AI models.
That’s where Qualys TotalAI comes in, extending the platform’s deep cloud visibility into the AI layer to help teams detect and mitigate emerging threats tied to model behavior, data flow, and governance.
And because TotalAI builds on the same unified platform that powers Qualys TotalCloud, organizations can seamlessly connect cloud posture insights with AI workload protection for end-to-end visibility and control.
Some examples of risks that require AI-aware cloud discovery:
- Shadow AI: Detection of unapproved AI workloads and tools used without security oversight.
- Missing guardrails: Which could expose generative AI systems to prompts that leak sensitive data.
- Misconfigured access controls: For example, training data stored in open S3 buckets or internet-exposed databases.
- AI governance misalignment: It could lead to non-compliance with various AI governance frameworks such as OWASP top 10 for LLM, NIST AI Risk Management Framework.
This is where TotalAI adds AI-specific insight, correlating infrastructure context with model-level risk. The result is a more complete, proactive defense for today’s AI-driven cloud environments.
Check out Qualys’s recent whitepaper, which offers actionable strategy and insights on how to responsibly secure AI in enterprise environments.
How TotalAI Secures the AI Lifecycle
Once you understand the new risks introduced by Cloud AI workloads like shadow AI, prompt injection, and model misuse, the next step is addressing them with precision.
This is where TotalAI operationalizes AI risk visibility. Built on the Qualys Enterprise TruRisk™ platform, it transforms fragmented insights into a unified, risk-based view across your AI footprint, so security teams can move from visibility to action.
Here’s how TotalAI delivers AI-specific coverage across the lifecycle:
AI Fingerprinting
TotalAI identifies and classifies:
- AI applications
- Model runtimes
- LLMs
- Inference frameworks
- AI libraries
- GPU infrastructure
It monitors both on-premises and cloud environments, correlates configurations, and flags potential exposures whether in AWS, Azure, or hybrid infrastructure.
AI Supply Chain Protection
TotalAI detects:
- Inconsistencies with model files and format
- Potential backdoors or extraction risks
- AI-specific integrity issues
This helps ML teams and developers make safer model choices and secure the entire AI build chain.
LLM Risk Scanning
Scan for:
- Prompt injections
- Hallucinations and bias
- Adversarial exploits and jail breaks
TotalAI also extends these checks to multimodal AI attacks, including those emerging from images, audio, and other formats beyond text.
Compliance mapping includes:
- OWASP Top 10 for LLMs
- MITRE ATLAS tactics and techniques framework
Guardrail Validation & Model Tuning
ML engineers can use TotalAI’s risk scanning to validate and calibrate AI guardrails. This includes simulating attack techniques to test model behavior and correlating cloud service policies (e.g., AWS Guardrails) with specific LLMs in use.
Review our TotalAI datasheet for the full capabilities at a glance.
Unified Security for Hybrid and Multi-cloud AI
Bringing it all together, Qualys TotalAI extends your existing CSPM capabilities into the AI layer, delivering unified AI risk management for on-premises, hybrid, and multi-cloud environments.
And it does so without the friction of new tooling. TotalAI seamlessly integrates with Qualys Agent, Scanner, and other sensors, eliminating onboarding overhead or integration challenges and providing immediate value.
What’s New in TotalAI 1.4.0
The latest release expands cloud platform support to include:
- Azure AI Foundry
- Azure OpenAI Service
- Azure AI Hub
- Azure Cognitive Services
Coming soon:
- AWS Bedrock
- AWS SageMaker
- Google Vertex AI
- Azure Machine Learning
Take Control of Your AI Security Posture
AI is powerful, but without oversight, it’s also risky. Qualys TotalAI gives you unmatched visibility, risk insight, and unified control over your AI workloads.
Ready to secure your cloud-hosted AI with confidence?
