Qualys TotalAI | Close AI Governance Evidence Gap

The AI Adoption Surge and Risk Explosion

Today, artificial intelligence is no longer a fringe innovation – it’s an enterprise imperative. Organizations are rolling out generative AI, LLMs, and autonomous agents at breakneck speed and already running millions of AI/ML workloads across their networks. But security has struggled to keep pace. As AI adoption accelerates, so does the threat surface, prompting a fundamental shift in cybersecurity priorities. Global statistics underscore the urgency. AI/ML-related breaches already affect 13% of organizations, while prompt-injection attempts have surged 340% year over year. In short, every chatbot, copilot, and agent expands the attack surface, demanding a proactive way to secure and govern this sprawling AI estate.

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Mounting AI Risks in the Enterprise

Unmanaged AI is now a growing business risk. Poorly governed LLMs can leak sensitive data, bypass safeguards, and be manipulated in seconds. One study found that LLM exploits succeeded in an average of 42 seconds, while one in five jailbreak attempts bypassed controls.

The risk is just as serious inside the enterprise. Employees are adopting unsanctioned AI tools, creating shadow AI and new paths for data loss. IBM found that organizations that used high levels of shadow AI observed an average of $670,000 in higher breach costs than those with a low level or no shadow AI. Where exposed information is sufficiently sensitive and the incident is deemed material, unauthorized AI use may also trigger SEC disclosure obligations.

At the same time, attackers are using AI to scale phishing, extract sensitive information, and evade safeguards faster than most security teams can respond.

The AI Governance Headache

As a result, AI governance has rocketed to the CISO’s to-do list. Boards, auditors, and customers now demand clear policies on AI use, security, and ethics. Security teams report spending countless hours answering nearly identical questions for different stakeholders:

• Do you maintain an AI inventory?
• How do you test your models?
• What can your AI agents access?

Yet most teams still rely on spreadsheets, questionnaires, and manual evidence gathering. The result is slower adoption, duplicated effort, and little proof that controls actually work.

And the burden is felt across roles. Compliance and GRC managers juggle vendor questionnaires and internal audits. AppSec and DevOps teams struggle to embed AI checks into CI/CD pipelines. Cloud and identity teams scramble to map AI permissions. Meanwhile, CISOs and risk officers must translate all of this into board-level reports.

Global Regulatory Pressure

Governments and regulators are adding to the urgency. Countries around the world are codifying AI requirements, demanding risk management and demonstrable controls:

• The EU AI Act (fully enforced August 2026) establishes strict obligations for “high-risk” AI systems, including risk assessments, logging, documentation, human oversight, and cybersecurity measures. Violations can incur fines up to €15 million or 3% of global turnover.
• In the U.S., NIST’s AI Risk Management Framework (AI RMF) is becoming the de facto baseline for trustworthy AI.
• States like California and New York are already linking AI laws to NIST guidelines.

Similarly, frameworks from OWASP (Top 10 for LLMs and MCPs), MITRE ATLAS, ISO/IEC 42001 (AI governance), Singapore’s AI guidelines, CERT-In, and others are proliferating.

These regulations all hinge on risk management and evidence. The core question is simple: How do you know your AI is safe?

That requires the same fundamentals as IT security, including inventory, scanning, remediation, and incident response, backed by continuous technical evidence that those controls are working. Policy documents alone won’t suffice. Organizations must continuously show that models and agents are tested, monitored, and controlled in production.

The Missing Layer in AI Governance: Evidence Gap in AI Controls

Traditional security tools can scan infrastructure and monitor data movement, but they cannot see how models and agents behave. A fully patched stack can still expose sensitive data through a crafted prompt, with no obvious infrastructure failure. Traditional “snapshot” assessments are blind to AI’s runtime behavior.

AI risk appears at runtime, through prompts, model responses, agent actions, and excessive permissions. Without visibility from code to cloud to browser, organizations cannot answer basic questions:

• Where exactly is AI running?
• Which models can leak or be manipulated?
• What can our agents do?
• Can we prove that our controls really worked?

No single category tool covers the end-to-end AI lifecycle. API scanners test known models but miss shadow AI. EDR and CNAPP platforms assess infrastructure, not inference behavior. CI/CD tools find code issues but cannot enforce runtime safety.

The core problem is the lack of ground truth: compliance frameworks tell companies what to do, but not how to prove it. Spreadsheets and periodic assessments cannot provide that proof. Boards, auditors, and regulators now expect verifiable evidence, making a unified technical approach essential.

Closing this proof gap requires more than another point solution. Qualys TotalAI brings AI security and governance onto a unified platform, turning continuous technical telemetry across the AI lifecycle into defensible evidence of control.

Qualys TotalAI: Four Pillars of AI Governance

Built on the Qualys Enterprise TruRisk Platform, Qualys TotalAI, a complete code-to-runtime AI security and governance solution, unifies four critical components into one workflow:

1. Discovery: Complete AI Inventory

True governance starts with knowing exactly where AI exists.

Qualys TotalAI continuously discovers sanctioned and shadow AI across cloud services, GPUs, containers, code repositories, SaaS copilots, browser plug-ins, AI agents, and MCP servers. It fingerprints models, frameworks, and workloads, then maps each asset to its owner, operating context, exposure, and access to critical systems. This replaces static spreadsheets and self-reported inventories with a live, auditable governance baseline.

The result is a contextualized AI inventory that shows what is running, where it runs, who owns it, and whether it is approved, giving security teams and auditors the evidence they need.

2. Assessment: Comprehensive AI Risk Analysis

TotalAI evaluates risk at every stage. TotalAI’s assessment gives a 360° AI risk picture, combining VM, AI-specific tests, and live-monitoring, with full forensic evidence and severity ratings for each finding.

TotalAI extends vulnerability and posture management to AI assets. It then tests how MCP servers, models, and agents behave. In production, it monitors what AI agents access. Browser and AI SaaS evaluation also reveals how employees use generative AI and whether sensitive data is being uploaded.

Posture: Understand what is exposed and misconfigured

• AI Security Posture: Identify failed security and governance controls, cloud misconfigurations, vulnerable AI infrastructure, excessive access, exposed model or MCP endpoints, missing safeguards, and toxic combinations of privilege, sensitive data, exposure, and weak controls.
• AI SaaS governance: Assess supported AI SaaS configurations, users, groups, access, and control posture while identifying sanctioned and shadow AI usage.
• AI Code Security: Detect exposed secrets, vulnerable dependencies, unsafe AI patterns, weak Cedar policies, and misconfigurations in repositories and CI/CD pipelines before deployment.

Behavior: Test whether AI can be manipulated

• LLM adversarial testing: Test models for prompt injection, jailbreaks, sensitive-information disclosure, hallucinations, unsafe output, multilingual and encoded attacks, denial of service, and multimodal manipulation.
• MCP and agentic AI testing: Scan MCP servers directly for tool poisoning, argument injection, SSRF, DNS rebinding, rug-pull attacks, unsafe file access, spoofing, command execution, and credential exposure.
• Evidence-backed findings: Review prompts, responses, severity, pass/fail results, successful jailbreaks, and technical justification to understand why a control failed and what should change.

Runtime: See what AI actually does after approval

• Govern agentic applications across their full lifecycle: Connect code, Cedar policy, workload, runtime, and behavior to understand what was built, deployed, permitted, and executed. Kernel-level (eBPF) instrumentation reveals what AI workloads actually execute on servers, providing visibility that scanners and logs cannot provide.
• Workforce Usage: Combine Browser Plugin telemetry with SaaS connectors to reveal sanctioned and shadow AI usage, risky configurations, user exposure, and sensitive-data movement through prompts and uploads.
• Monitor AI after approval: Detect posture drift, abnormal model or MCP activity, suspicious workload behavior, and data-exposure indicators in production.
• Track deployed agents and AI workloads across supported hosts and container environments.

3. Remediation: Prioritized, Actionable Guidance

Findings alone aren’t enough; TotalAI automatically translates them into prioritized risk reduction.

Every AI issue is scored with the Qualys TruRisk engine, which factors in vulnerability severity, data sensitivity, asset criticality, business context, model behavior, and runtime evidence. This correlation means organizations can focus on the fixes that matter most (for example, a prompt injection on a model with access to customer PII gets top priority).

TotalAI then routes each issue, whether it’s a misconfiguration, exposed secret, weak access policy, or failed model test, to the right owner (cloud, DevOps, AppSec, identity/GRC teams).

TotalAI turns failed AI tests into guided, deployment-ready remediation. It preserves the attack evidence, recommends platform-specific guardrails, generates the required configurations, keeps human approval in the loop, and routes each issue to the right owner.

When behavioral controls fail, TotalAI can recommend changes to guardrails, prompts, retrieval logic, policies, or application design. It also provides actionable guidance for vulnerable software, cloud misconfigurations, excessive access, unsafe MCP tools, weak policies, exposed secrets, and failed model tests. For example, if a model scan identifies a successful jailbreak, TotalAI may recommend adding a guardrail or retraining the model with adversarial prompts.

Once the fix is implemented, TotalAI retests the system and records the outcome. This creates defensible evidence that the risk was reduced and closes the loop from detection to remediation, verification, and documentation.

4. Governance: Audit-Ready Reporting

Above all, TotalAI generates continuous technical evidence for compliance and oversight. It consolidates the entire AI inventory, posture scan results, test logs, and remediation status into unified dashboards and reports. These outputs directly address the questions auditors and executives care about: “What AI systems do we have? Which ones are compliant? Which issues remain open?”

TotalAI can export framework-aligned reports that map controls and findings to NIST AI RMF, EU AI Act requirements, FISMA, CMMC 2.0, OWASP Top 10 for LLM, MITRE ATLAS, etc.

TotalAI transforms day-to-day security operations into repeatable proof for customer security questionnaires, vendor assessments, board decks, and audit submissions.

For federal agencies and contractors, TotalAI is FedRAMP Moderate Authorized and deployable within an authorized boundary with no additional ATO effort.

Sign up for a no-cost trial and experience the power of TotalAI for yourself.

TotalAI is about proving AI governance is working in real time. This means organizations can walk into a board meeting or regulatory exam with a single source of truth showing exactly how AI risks have been managed.

Throughout these pillars, TotalAI’s architecture delivers unique advantages. It’s built on a single platform that already handles cloud, vulnerability, and container risk – so AI risk is simply another dimension of your existing security program. No extra infrastructure or agents are needed, and findings flow directly into familiar workflows. TotalAI also covers gaps that stand-alone tools miss: for example, most products stop at inventory or red-teaming, whereas TotalAI ties it all together end-to-end. Kernel instrumentation and SaaS connectors give data that normal scanners can’t (as noted, “your existing stack sees around this, not through it”). In sum, TotalAI delivers a comprehensive, proactive, risk-based approach – from “shift-left” code checks to cloud monitoring to runtime AI vigilance – all in one unified dashboard.

The Shift That Will Define AI Risk Management for Modern CISOs

As AI becomes embedded across the enterprise, security teams need more than visibility.

The AI security conversation has moved beyond basic capability. The question is no longer, “Can you scan a model or find shadow AI?” It is, “Can you prove your AI is secure to your board, auditors, regulators, and customers?”

AI can no longer be treated as a governance exception. TotalAI makes AI security a disciplined, continuous part of enterprise risk management.

TotalAI provides the complete solution for enterprise AI governance: discover all your AI, assess its posture and behavior, remediate risks intelligently, and generate continual proof of compliance.

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Frequently Asked Questions (FAQs)

Q: What is the AI governance evidence gap?

A: Most organizations have AI policies and questionnaires but lack continuous technical evidence that models, agents, and AI workloads are actually secure, tested, and controlled in production.

Q: How does Qualys TotalAI differ from traditional security tools?

A: Traditional tools assess infrastructure or code but cannot see model behavior, agent actions, or runtime AI risk. TotalAI unifies discovery, behavioral testing, runtime monitoring, and audit-ready reporting across the full AI lifecycle.

Q: Does TotalAI require new agents or infrastructure?

A: No. TotalAI uses existing Qualys Cloud Agents and cloud integrations. No new agents, code modifications, or additional infrastructure are required.

Q: Which regulations and frameworks does TotalAI support?

A: TotalAI generates framework-aligned reports that map to NIST AI RMF, the EU AI Act, OWASP Top 10 for LLMs, MITRE ATLAS, FISMA, CMMC 2.0, and other major requirements. It is also FedRAMP Moderate Authorized.