Request a Free Security Assessment
ZeroCeption Logo
ContactBlog
AI Security Posture Management (AI-SPM)

Secure Every Model, Agent, and Prompt with Zeroception

Zeroception's AI-SPM delivers continuous discovery, data inventory, identity governance, and risk scoring across your entire AI estate. Catalog every AI application, MCP agent, and LLM data source, secure prompts and training data, and enforce Zero Trust controls from foundation model to production copilot.

Secure Every Model, Agent, and Prompt with Zeroception - Product Image
Click to expand
Trace Every Hop — From Raw Data to Live Inference
Click to expand
Endpoint Lineage & Data Flow

Trace Every Hop — From Raw Data to Live Inference

Zeroception's endpoint lineage map visualizes the full data flow across your AI estate: source systems feeding ingestion jobs, embedding stores powering retrieval, prompt templates invoked by applications, MCP agents calling tools, and foundation models returning inferences. Pinpoint where sensitive data enters, transforms, and exits your AI pipeline — then enforce residency, retention, and exposure controls at every hop.

AI-Specific Risk Findings

AI-SPM Findings & Insights

Zeroception goes beyond traditional posture management to address risks unique to generative AI: shadow LLM usage, prompt injection, training data poisoning, model theft, unsanctioned MCP agents, and sensitive data leakage into third-party inference endpoints. Every finding includes severity, affected assets, and step-by-step remediation.

AI-SPM Findings Dashboard - Prioritized AI-specific risks, policy violations, and prompt exposure
AI Findings Dashboard
Click to expand

Shadow AI & MCP Agent Discovery

Automatically inventory every AI application, copilot, and MCP (Model Context Protocol) agent running across your environment. Detect shadow LLM usage, unapproved providers, and rogue agents before they reach sensitive data.

Prompt & Training Data Inventory

Catalog every prompt, embedding, and fine-tuning dataset in use. Classify sensitive content, flag PII leakage into prompt logs, and track which AI systems consume which data sources.

Access Exposure Analysis - Identify sensitive prompt data exposed to AI identities and third-party providers
Prompt Access Exposure
Click to expand

End-to-End AI Pipeline Governance

Map every hop in your AI supply chain — from data source to embedding store, from foundation model to agent, from tool call to response. Enforce guardrails on prompt injection, model theft, training data poisoning, and unauthorized tool invocation. Map controls to NIST AI RMF, ISO/IEC 42001, OWASP LLM Top 10, and the EU AI Act.

AI Identity & Access Governance

Inventory human and non-human identities interacting with AI systems — service accounts, agent tokens, API keys, and OAuth grants. Detect over-privileged access to prompts, training data, and model endpoints.

Continuous Risk Scoring

Every AI asset is scored on data sensitivity, exposure surface, model provenance, and identity blast radius. Prioritize remediation with severity-ranked findings and executive-ready posture metrics.

AI Security Posture Management (AI-SPM)

AI Estate Governance

Go Beyond AI-SPM

ZeroCeption's AI-SPM is purpose-built for the generative AI era. We help you eliminate shadow AI, govern autonomous agents, and protect the data that flows through every LLM — so you can adopt AI at enterprise scale without inheriting a new attack surface.

Shadow AI Elimination

Automatically detect unsanctioned AI tools, unapproved foundation models, and rogue MCP agents across your organization. Bring every AI workload under visibility, policy, and audit before it touches sensitive data.

Prompt & Training Data Protection

Classify every prompt, embedding, and dataset feeding your AI systems. Prevent sensitive data from leaking into third-party model providers, block PII in prompt logs, and quarantine poisoned training inputs.

AI Compliance & Governance

Map your AI controls to NIST AI RMF, ISO/IEC 42001, the EU AI Act, and OWASP LLM Top 10. Produce audit-ready evidence of model inventory, data lineage, and access controls for regulators and executives.

AI-SPM Identities — Human and non-human identities with access to AI workloads

AI Data Sources — Applications, Agents & Data Map

AI Data Inventory & Access Exposure

Know Your Prompts. Control Your Access.

Modern AI risk lives at the intersection of data and identity. Zeroception's AI-SPM continuously inventories the prompts powering your AI workloads and the identities that can read, modify, or exfiltrate them — so you can answer the questions auditors, regulators, and your CISO actually ask.

  • Continuously catalog every prompt, system message, template, and retrieval context used by your applications and agents. Automatically classify prompts containing PII, PHI, PCI data, source code, credentials, and intellectual property. Track prompt distribution by provider, application, and owning team to understand exactly what data leaves your perimeter on every inference.

  • For every sensitive prompt or dataset, Zeroception maps the full exposure graph: which human users can read it, which service accounts can modify it, which agents can retrieve it as context, and which external model providers receive it on inference. Surface over-privileged access, cross-tenant leakage paths, and prompts that route regulated data to unapproved endpoints.

  • Manage Model Context Protocol agents as first-class assets. Register agent capabilities, enumerate tool scopes, verify signing and provenance, and monitor invocation patterns in real time. Detect agents with excessive permissions, abnormal tool-call graphs, and unauthorized escalation paths before they impact production data.

  • Visualize how data flows through your AI estate end-to-end — from source systems, through ETL and embedding pipelines, into vector stores, through foundation models, and back into downstream applications. Trace sensitive data lineage, verify retention policies, and enforce data residency requirements across every AI workload.

AI Data Inventory — Prompts catalog with classification, sensitivity, and distribution