Resource Center

AI Security Research,
Guides & Tools

Deep research, real incident analyses, and practical guides for governing autonomous AI — from the RuntimeAI security team and independent analysts.

Analyst Research

Framework · Stanford + MVP Ventures

SACR Framework — Independent AI Security Analysis

Stanford GSB + MVP Ventures researchers independently described what RuntimeAI built. Three governance layers. 15 vendors analyzed. RuntimeAI: 10/10 — the only vendor covering all three layers.

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Analyst Reports

Third-Party Validation & Market Research

Independent analyst coverage and market research validating RuntimeAI's approach to AI agent governance.

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Product Pamphlets

PDF · Platform Overview

RuntimeAI Platform Overview

The full platform in one document — discovery, policy enforcement, kill switch, and audit trail for every AI agent and non-human identity.

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PDF · Strategic Briefing

RuntimeAI Strategic Briefing

A board-level briefing on AI agent governance — the stakes, the five outcomes, and where to start. For CISOs, CIOs, and the board.

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PDF · Agent Governance

AI Agent Governance Pamphlet

Identity, inline policy enforcement, and the kill switch — the technical detail behind governing autonomous agents at runtime.

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PDF · Financial Services

RuntimeAI for Financial Services

Fraud scoring inline, regulatory-mapped audit trails, and compliance-ready evidence for banks and financial institutions deploying AI agents.

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PDF · Compliance

GRC & Compliance Guide

How RuntimeAI auto-maps agent behavior to your active compliance frameworks — continuous evidence generation, not audit-time scrambling.

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PDF · Cost Guide

Budget Displacement Guide

Where unmanaged AI agent sprawl hides in your budget, and what agent identity and kill-switch controls actually displace.

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Guide · SMB Solutions

RuntimeAI for Growing Businesses

Shadow AI discovery, a governed LLM gateway, cost control, and agent-building APIs — enterprise-grade AI governance without the enterprise price tag or complexity.

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Security Guides

AI Agent Security — Best Practices Guide

Comprehensive guide to governing autonomous AI agents: identity, policy, enforcement, monitoring, and incident response across enterprise deployments.

PQC Readiness Guide for AI Infrastructure

Post-quantum cryptography transition guide for AI systems. ML-KEM, ML-DSA, and SPHINCS+ implementations for agent identity, signing, and in-transit protection.

MCP Security — Securing Model Context Protocol

53% of public MCP servers have hardcoded secrets. This guide covers MCP threat model, SPIFFE mTLS for MCP, supply chain verification, and shadow server detection.

Incident Analysis & Research

Incident Analysis

Vercel AI SDK Incident — What Broke and Why

Deep-dive analysis of the Vercel AI SDK incident. What governance controls were absent, how RuntimeAI's enforcement layer would have caught it, and lessons for the industry.

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Threat Research

LiteLLM Supply Chain Attack — MCP Threat Taxonomy

How attackers are targeting AI integration layers. Complete threat taxonomy for MCP supply chain attacks and mitigation strategies using the AI Integration Fabric.

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AI Datacenters — The Governance Gap

As AI factories scale to thousands of agents on GPU infrastructure, the missing layer is governance. What needs to be built above the compute provisioning layer.

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Physical AI Governance — Robots Need Kill Switches Too

Applying AI agent governance to physical systems: robots, drones, and autonomous vehicles. Hardware attestation, geofencing, and emergency stop protocols.

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Quantum Day 2026 — Post-Quantum Cryptography for AI

NIST PQC standards are final. What does ML-KEM and ML-DSA mean for AI agent identity, signing, and in-transit security? The complete migration guide.

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Weekly AI security digest, incident analyses, research papers, product updates, and governance thought leadership from the RuntimeAI team.