Qualys TotalAI

TotalAI builds a continuously updated inventory of AI across the enterprise using the Qualys sensors and connectors already deployed. TotalAI brings multiple Qualys discovery mechanisms into one AI inventory across multi-cloud, on-premises, containerized and workforce environments without building separate inventories for each.

TotalAI discovers shadow AI from technical signals rather than questionnaires to find unapproved models, AI packages, local model-serving stacks, browser-based shadow AI visibility and other AI infrastructure that application owners may never have registered.

CSPM/CNAPP tells you whether the underlying cloud and workload infrastructure is secure; AI Security Posture Management extends that assessment into AI-specific resources and controls. A cloud resource can therefore be technically compliant from a traditional CSPM/CNAPP perspective while the AI service running on it still has unsafe access, missing guardrails or exploitable model behavior. TotalAI answers the question: what AI is running there, how is it configured, and does the model itself behave safely?

TotalAI allows teams to onboard a model endpoint and run repeatable adversarial scans before deployment, including from CI/CD or MLOps pipelines. The tests send controlled attack prompts to the model and evaluate its responses for AI-specific weaknesses. The resulting pass/fail evidence can be used as a release criterion, before the model reaches production.

MCP changes the risk model because it gives AI agents a standardized way to reach tools, data and privileged enterprise functions. A compromised or misleading MCP server can turn an otherwise benign agent interaction into an action against an internal system. It can change the problem from “the model produced a bad answer” to “the agent performed an unsafe action using legitimate permissions.” TotalAI tests for risks including tool poisoning, argument injection, SSRF, DNS rebinding, unsafe browser/file access, rug-pull behavior, cross-server shadowing, server spoofing, command execution and credential exposure.

TotalAI approaches workforce GenAI as both a usage-discovery and SaaS-posture problem. two complementary paths: browser AI telemetry to see how employees use browser-based AI, and AI SaaS posture/compliance to assess enterprise AI SaaS configuration through connectors/APIs. Security teams can use the evidence of which AI services employees use, who uses them and whether the corresponding enterprise controls are configured appropriately.

TotalAI’s Browser Plugin provides visibility at the point where employees interact with web-based AI applications. It watches AI-specific browser activity and sensitive-data movement at the point of use, including use of tools such as ChatGPT and Claude.

TotalAI uses layered MCP discovery rather than depending solely on manually registered MCP endpoints or a registry of known servers. It probes for MCP protocol behavior and identifies locally accessible MCP servers and confirms runtime usage.

TotalAI sends direct and indirect adversarial prompts designed to determine whether untrusted instructions can override/manipulate the intended behaviour or instructions or security controls of the model/application. Every detection retains prompt-level evidence so teams can see which test succeeded rather than receiving only a generic vulnerability label.

TotalAI moves beyond assessing how an agent was configured and observes what it actually executes in production. It combines kernel-level runtime visibility with agent and MCP context, including the tool calls an agent makes and the systems those tools can reach. Security teams can therefore connect what was coded and permitted with what the deployed agent actually accessed or executed, and identify abnormal activity after approval.