AI Governance Platform
+ Security Advisory
The AI governance platform that combines continuous red teaming, adaptive guardrails, and zero trust enforcement — so your AI systems are secured, monitored, and audit-ready by default.
Our Mission
We are building the AI governance platform that enterprises need — one that combines continuous red teaming, adaptive guardrails, and zero trust enforcement into a single, always-on system rather than a collection of point solutions.
Our advisory practice is how we work with clients today — solving immediate AI security challenges while co-developing the platform capabilities that will automate this at scale. Every engagement directly shapes our product roadmap.
Why Consulting Alone Falls Short
AI systems are non-deterministic and continuously evolving. A one-time assessment or a periodic audit leaves you exposed the moment your models update, your agents change behavior, or a new attack vector emerges. You need governance that runs as fast as your AI does.
Why a Unified Platform Changes Everything
Threats detected in hours, not the next quarterly review. Compliance evidence generated automatically, not assembled under audit pressure.
The Platform
Not point solutions. Not one-time audits. A continuously operating AI governance system that discovers threats, enforces boundaries, and verifies every action across your entire agentic stack.
Platform Output
All three engines feed a unified compliance dashboard. SOC 2, ISO 27001, and HIPAA evidence generated automatically — every agent action logged, scored, and audit-ready.
Every consulting engagement shapes the product roadmap. Join now to secure your AI systems today and co-build the platform that will automate it at scale.
From Architecture to Implementation
We translate this 3-layer architecture into actionable engineering outcomes through our core consulting pillars.
Layer 1
Layer 2
Layer 3
The Compliance Problem
Advisory Services
Access the platform’s three core capabilities today through expert advisory — while we build the automated system that delivers them continuously.
Delivered today via advisory → automated in the platform
Expert-led adversarial testing that mirrors what the platform will automate — prompts, agents, retrieval systems, tool abuse, and data leakage under realistic attack pressure.
Delivered today via advisory → automated in the platform
We design and deploy the guardrails architecture for your stack — the same defense-in-depth controls the platform will enforce continuously without manual intervention.
Delivered today via advisory → automated in the platform
We implement zero trust policy and generate the compliance evidence your auditors require — the same audit trail the platform will produce automatically, continuously.
Engagement Models
All advisory tiers include priority placement on the platform early access program. Build with us now — get the automation first.
Ongoing AI security support to build and mature your defenses. 10–40 hours/month depending on tier.
~10 hrs/mo — foundational AI security guidance
~20–30 hrs/mo — deep engagement reviewing the security posture of AI & providing advisory
~40 hrs/mo — full-spectrum program: red teaming, adversarial defense, compliance & AI Supply Chain security.
Best for one-time security assessments, red team engagements, and compliance preparation with defined deliverables.
2 weeks minimum — scope-dependent
Ad-hoc advisory, intermittent security policy review, or expert consultation on demand. No long-term commitment.
Billed in 1-hour increments
Client Onboarding
Submit the first signal. ZTAI.AI will review your AI architecture, identify the likely threat surface, and prepare an assessment path.
Security Intelligence
Research and insights on AI threat modeling, adversarial defense, and compliance for agentic systems.
A systematic classification of prompt injection vectors across direct, indirect, and adversarial RAG attack surfaces in multi-agent pipelines.
Dynamic attack-path mapping specific to LLM behavior, API bindings, and data sources—built for teams that can't rely on static code review.
Extending classical Zero Trust networking—never trust, always verify—to every LLM call, tool execution, and data flow in your agentic stack.
An enterprise tech firm eliminated unauthorized AI tool usage—specifically Claude Code running under personal credentials—by deploying a corporate AI sandbox with EDR and SIEM integration. Achieved 100% AI usage visibility and SOC2/GDPR audit-readiness without slowing developers.
Featured Case Study
By Benedict Kwok — Founder & Principal Security Advisor, ZTAI Security Advisors LLC
Executive Summary
An enterprise-level tech firm eliminated Shadow AI risks—specifically unauthorized AI tools like Claude Code running under personal credentials—by implementing a secure corporate AI sandbox and deploying targeted EDR and SIEM rules to detect personal API key usage. Using tools like Microsoft Defender KQL and CrowdStrike regex, the firm achieved 100% visibility of AI usage, became audit-ready for SOC2/GDPR, and maintained developer velocity.
About ZTAI Security Advisors
ZTAI helps enterprises adopt AI securely. We offer two complementary approaches:
In an era where AI is reshaping business operations, the rise of shadow AI—the unauthorized use of AI tools by employees without organizational oversight—has emerged as a critical cybersecurity and compliance risk. According to the IBM Cost of a Data Breach Report 2025, incidents involving shadow AI added an average of $670,000 to the total cost of a data breach compared to breaches that did not involve unapproved AI tools.
Shadow AI is not a hypothetical threat—it's a reality. Developers, analysts, and even executives may use personal API keys or unapproved AI tools to expedite workflows, unaware of the potential for data exfiltration, compliance violations, or system sabotage.
Goal: Translate AI security risks into compliance, audit, and operational concerns.
Shadow Infrastructure / Unmonitored Code Execution
“Claude Code is a powerful autonomous agent with shell execution capabilities. When developers use personal API keys, it becomes an unmonitored code execution gateway—equivalent to allowing shell access without IT oversight. This creates visibility gaps that compliance frameworks like SOC2 and ISO 27001 explicitly prohibit.”
System Destruction Risk
“Autonomous code execution without governance can lead to unintended data modifications, production database changes, or system instability. Organizations need centralized oversight of all AI-assisted code modifications.”
Compliance Violations
“Personal API keys break our data handling commitments for SOC2/ISO 27001/GDPR. If auditors ask for logs, we'll fail because there's no centralized visibility.”
Outcome: IT prioritizes risks that directly impact compliance, legal exposure, or operational stability.
Goal: Give IT a clear, actionable path to mitigate risks without deep technical expertise.
EDR Rule (Microsoft Defender KQL)
Flags Claude process executions originating outside your approved device group:
DeviceProcessEvents
| where ProcessVersionInfoFileName =~ "claude" or ProcessName contains "claude"
| where not(DeviceName in ("Approved-AI-Dev-01", "Approved-AI-Dev-02"))
| project TimeGenerated, DeviceName, AccountName, FileName, FolderPath, ProcessCommandLine
Regex for CrowdStrike (API Key Detection)
Catches standard Anthropic personal API key formats in environment variables or command lines:
\bsk-ant-sid\d*-[A-Za-z0-9_-]{32,}\b
SIEM Regex for High-Risk Data (Prompt Monitoring)
Catches high-risk data types in logs or command lines. Note: may produce false positives on security training materials—manual tuning per organization is recommended.
(?i)\b(payroll|ssn|social[- ]?security|performance[- ]?review|termination|financials)\b
Automate .claudeignore Deployment
PowerShell script that prevents Claude Code from accessing sensitive directories across user profiles:
$IgnorePath = "$env:USERPROFILE\.claudeignore"
$IgnoreContent = @(
"**/HR/**",
"**/Finance/**",
"**/*.xlsx",
"**/*.csv",
"**/.env*"
)
Set-Content -Path $IgnorePath -Value $IgnoreContent -Force
Outcome: IT can deploy blocks with minimal effort, avoiding delays or resistance.
Goal: Position security as an enabler of innovation, not a blocker.
Corporate AI Sandbox
“We want you to move fast with AI. We're setting up a company-wide API key with high usage limits—no personal costs, no billing surprises. Just route requests through our secure dev gateway first.”
Vendor-Recommended Solutions
“Cloud providers and AI vendors explicitly recommend enterprise-grade API key management for production use. We're implementing industry best practices to avoid environmental corruption.”
Velocity & Uptime Focus
“Personal API keys risk project delays if accounts get flagged. Centralizing keys under a corporate tier ensures unlimited runtime and maximum speed.”
Outcome: Security becomes a strategic enabler, not a restriction.
Goal: Establish shared responsibility and create a compliance paper trail.
Formal Risk Assessment Email
Subject: AI Security Risk: Unmonitored AI Tool Usage To: [IT Lead] CC: [Direct Manager] Issue: Developers are running autonomous AI tools (like Claude Code) using unmonitored personal credentials, creating visibility gaps and compliance exposure. Impact: Potential for silent data exfiltration, compliance failure, or unintended system modifications. Recommendation: Block personal-key execution via EDR and mandate a corporate-monitored gateway for all AI-assisted development activities. Next Steps: Please confirm if IT accepts this risk or if you need technical implementation blocks to deploy mitigation. --- [Your Name] AI Security Advisor
Outcome: Legal and professional responsibility is documented. If risks materialize, you've established that the issue was flagged and the decision to defer action was made upstream.
Goal: Detect and respond to risky behavior proactively.
AuditLog | where OperationName contains "Anthropic" | project TimeGenerated, UserPrincipalName, OperationName, Result
Outcome: Early detection of deviations from normal behavior allows rapid response.
Once you deploy a corporate sandbox and EDR rules, the next challenge is continuous monitoring—detecting shadow AI usage, auditing prompts in real-time, and staying compliant as your AI adoption scales. This is where governance automation becomes essential.
Your AI Security Roadmap
Phase 1 — Immediate
Corporate sandbox + EDR rules + .claudeignore deployment
Phase 2 — 3–6 Months
Continuous monitoring with SIEM rules and alert tuning
Phase 3 — 6–12 Months
Automated governance and continuous red teaming
Shadow AI isn't inevitable—it's a sign that governance hasn't kept pace with adoption. The organizations that win at AI are the ones that make security frictionless, not bureaucratic.
Whether you're starting with manual controls (KQL rules, corporate sandboxes) or ready to automate governance end-to-end, ZTAI Security Advisors helps you build a sustainable, scalable AI security program.
AI Security Assessment
Identify shadow AI risks and design zero-trust controls. 30-minute guided assessment with remediation roadmap included.
Early Access: Governance Automation
Continuous red teaming and automated prompt injection audits. Join our early access program for AI governance at scale.
Featured Research
By Zata Security Advisors LLC Research Team
The software security community has relied on the Common Vulnerabilities and Exposures (CVE) standard for decades. CVE was designed for a world of deterministic software—where vulnerabilities are discrete, reproducible bugs in code that can be patched. In that model, a fix is clear: update a binary, patch a library, increment a version.
Agentic AI systems break this model entirely.
An LLM agent's attack surface isn't a memory corruption bug—it's a behavioral space. The same model, with the same weights, may respond safely to one prompt and dangerously to a subtle variation. This isn't a code defect. It's a property of the reasoning layer's probability distribution, shaped by training data, RLHF fine-tuning, and contextual state.
CVE requires a discrete, reproducible vulnerability tied to a specific software version. AI vulnerabilities are often:
The Agentic Vulnerability Exposure (AVE) standard is ZTAI.AI's proposed taxonomy for classifying, scoring, and tracking security exposures in AI systems. AVE operates alongside CVE—it does not replace it. Where CVE handles infrastructure and dependency vulnerabilities (a patched MCP server, a vulnerable API library), AVE handles behavioral and reasoning-layer risks.
An AVE entry describes a repeatable attack pattern against an AI agent's reasoning, orchestration, or execution layer—documented in natural language, with reproducibility measured by probability distribution rather than deterministic reproduction.
Each AVE entry is classified across five dimensions:
The security industry has relied on the Common Vulnerability Scoring System (CVSS) for decades to quantify infrastructure and software risk on a 0–10 scale. CVSS scores attack vector, attack complexity, privileges required, user interaction, scope, and impact — metrics that map cleanly onto deterministic code vulnerabilities with a clear patch path.
CVSS is not broken. For what it was designed to do — scoring a memory corruption bug, a misconfigured API endpoint, or a vulnerable library — it remains the right tool. The problem is that AI behavioral vulnerabilities are a fundamentally different class of risk that CVSS was never built to express.
ZTAI.AI's AI Vulnerability Scoring System (AIVSS) does not replace CVSS — it transforms its scoring philosophy for AI behavioral exposures. AIVSS inherits CVSS's 0–10 numeric output and its commitment to standardized, comparable scoring. What changes is the input dimensions, which are redesigned for non-deterministic, language-driven systems.
The two standards are designed to coexist. A chained attack that exploits both an infrastructure flaw and an LLM behavioral weakness would carry both a CVSS score and an AIVSS score — each measuring a distinct attack surface:
AIVSS produces a 0–10 score across four AI-native dimensions that have no equivalent in CVSS:
For teams operating AI in regulated environments (healthcare, finance, defense), AVE + AIVSS provides the evidence framework needed for AI security audits. A finding documented in AVE format gives an auditor:
The security industry is at an inflection point. CVE and CVSS remain essential — they are not going away. AVE and AIVSS are the missing layer that CVE and CVSS were never designed to address. Together, all four standards give security teams a complete picture: code-layer risk scored by CVSS, behavioral-layer risk scored by AIVSS, with CVE and AVE providing the classification vocabulary for each. As agentic AI becomes core infrastructure, organizations that adopt this complete framework now will be best positioned to meet the compliance and audit requirements of tomorrow.
Get In Touch
Have a question about AI security, our services, or want to explore a partnership? Send us a message and we'll respond within one business day.
contact@ztai.ai
Response Time
Within 1 business day
Location
Remote — Serving clients globally