AI Security
DynamoGuard
Runtime AI guardrails that block jailbreaks, prompt injection, and PII leakage in LLM apps.
DynamoGuard Overview
What it does
DynamoGuard is a runtime AI guardrail product that enforces security, safety, and compliance controls on large language model (LLM) applications. It translates natural-language governance requirements into custom, low-latency guardrails that inspect prompts and model outputs in real time, blocking jailbreaks, prompt injection, personally identifiable information (PII) leakage, and toxic content across 15+ categories. Guardrails are trained with a synthetic-data methodology and run on CPU for on-device, browser-extension, or in-environment deployment.
How it works
Policies are authored in natural language and compiled into guardrail models that classify inputs and outputs against content, keyword, and regulatory rules, including EU AI Act criteria. The product pairs runtime enforcement with real-time hallucination detection and root-cause analysis, human-in-the-loop review, and observability that logs AI interactions for audit. Guardrails are continuously stress-tested against jailbreak and prompt-injection techniques through the companion DynamoEval red-teaming module. CPU-based inference lets the guardrails deploy as a browser extension, inside a customer virtual private cloud, or fully on-premises, so sensitive data never leaves the customer environment.
Credentials and traction
DynamoGuard maintains SOC 2 and ISO 27001 security certifications. Named enterprise deployments include Qualcomm, Intel, Lenovo, and Experian, with Intel embedding the guardrails into consumer AI PCs and Itochu Techno-Solutions applying them to strengthen generative AI compliance for financial institutions. The platform targets regulated industries including financial services, insurance, payments, and semiconductors, and counts the U.S. Army among its published government users.
Key Capabilities
mapped to solution categoriesDetects and blocks adversarial inputs designed to override system prompts, extract training data, or redirect model behavior. Detection approaches include pattern matching, input semantic analysis, and secondary model classification.
Intercepts prompts and completions to prevent sensitive data (PII, credentials, internal IP), from being transmitted to external LLM services or returned in model responses.
Evaluates model outputs against content policy, data classification rules, and format expectations before delivery to end users, blocking responses containing sensitive data or policy violations.
Monitors model outputs for factual inconsistency and hallucination, relevant for AI applications where response accuracy has legal, financial, or safety implications.
Records prompts, completions, and metadata for all AI interactions with tamper-resistant storage, supporting compliance, forensics, and policy investigation.
Continuously stress-tests the product's own guardrails and filters against jailbreaks, prompt-injection payloads, and data-extraction attempts, then re-tightens policies after model or prompt changes. A self-validation loop within the runtime protection layer, distinct from the standalone AI Red Teaming discipline that tests AI systems end to end.
Implementation & support
Info last updated on August 1, 2026
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