Gray Swan is an AI agent security platform for enterprises that deploy models and autonomous agents. It targets failures that perimeter defenses and data loss prevention tools miss. These include prompt injection, jailbreaks, data exfiltration, context bleed, and ungoverned tool usage.
Shade handles pre-deployment red-teaming. It uses LLM-based adversarial agents, delivered as automated software, to attack your models, agents, and guardrails in your own deployment context.
Cygnal handles runtime enforcement. It classifies and blocks adversarial inputs and unsafe outputs in production, and monitors multi-step agent tool interactions.
Both products draw on the Gray Swan Arena, a red-teaming network of 15,000+ researchers. Its members have generated over three million attack attempts that inform the platform’s methods.
Pricing
Gray Swan does not publish pricing, plan names, or usage-based rates. No free plan or free trial is listed. Access starts by scheduling a demo or speaking with a security expert. Feature gating by tier is not disclosed.
* Disclaimer: Please note that pricing information may not be up to date. For the most accurate and current pricing details, refer to the official website.
Key Features
- ✓
Automated red-teaming of models, agents, and guardrails with Shade
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Real-time input and output filtering in production with Cygnal
- ✓
Behavioral policy enforcement across multi-step agent tool calls
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Regression testing after model, prompt, and policy changes
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Detection of goal hijacking and instruction override attempts
Use Cases
Prevent AI Data Breaches
A customer-facing assistant with file access can leak PII through context bleed. Cygnal inspects data flows and blocks exfiltration paths. Shade simulates extraction techniques before launch.
Govern Autonomous Agent Behavior
Agents that call APIs and query databases need clear operational limits. Cygnal applies role-based, context-aware policies at runtime. Shade tests for unauthorized actions before production.
Safeguard Brand Trust
Public chatbots can produce harmful, biased, or off-brand replies. Cygnal filters policy and tone violations in live conversations. Shade stress-tests the system on controversial topics.
Catch Failures Before Production
Brittle guardrails and edge-case weaknesses often surface only after launch. Shade probes deployments with evolving attacks beforehand. Cygnal then flags anomalies in production.
Harden Against Prompt Injection
Security teams can close attack surface exposed to jailbreaks and injection. Offensive simulation and runtime blocking work together. Arena findings feed both sides.
Strengths & Weaknesses
Strengths
Threat intelligence comes from a network of over 15,000 adversarial researchers.
One vendor covers both pre-deployment testing and runtime enforcement.
Python, JavaScript, and cURL client libraries fit existing developer stacks.
Focus on agent workflows and tool calls goes beyond basic toxicity filters.
Policies and scans adapt to each deployment’s tools, data, and workflows.
Weaknesses
Pricing tiers and cost structures are not published anywhere on the site.
Public technical documentation goes little beyond basic code snippets.
No self-serve onboarding exists, so buyers must contact sales first.
Performance claims on latency and block rates lack public quantitative detail.
Who Is This For?
Enterprise AI engineering and security teams: groups deploying agents and tool-connected LLMs who need protection against leaks and injection.
AI model builders and frontier labs: organizations that need adversarial evaluation before releasing a model.
AI security researchers: individuals who want to join Arena competitions, earn cash prizes, and practice red-teaming.
Compliance and risk officers: staff in regulated firms who need enforceable behavioral controls for AI insurance and regulatory readiness.
Frequently Asked Questions
What is the difference between Shade and Cygnal?
Shade is an autonomous pre-deployment red-teaming tool. Cygnal is a runtime layer that filters inputs and outputs and enforces behavior in production.
How do developers connect Cygnal to an application?
Requests route through a Gray Swan API base URL with authentication headers. Examples are shown for Python, JavaScript, and cURL.
How can a team get started with Gray Swan?
Book a demo or consultation, since no free trial or self-serve plan is listed. Teams then configure security policies and run Shade scans before enabling Cygnal.
Does it cover agents with tool and API access?
Yes. The platform targets agents that call tools, APIs, databases, and files, including goal hijacking and unauthorized actions.
Can researchers use Gray Swan without an enterprise contract?
Yes, through the Arena. It offers cash prizes, weekly red-teaming challenges, and a research hub of 150+ curated tools and datasets.
How does testing stay current after model updates?
Gray Swan describes continuous regression testing across model updates, prompt revisions, and policy changes. Arena findings also update its attack methods.
Does Gray Swan publish latency or block-rate benchmarks?
Not in the sources reviewed. The site makes qualitative performance claims without detailed public figures, so request data during a demo.
Which AI labs reference Gray Swan’s work?
Its evaluations have informed system cards for models from Anthropic, Google, OpenAI, and Meta. The site also displays logos of several partners and customers.
Does it help with compliance requirements?
Gray Swan lists compliance and governance as a deployment area, including AI insurance and regulatory preparation. Specific frameworks are not detailed in the source material.
Gray Swan connects through standard client libraries in Python, JavaScript, and cURL, using a Cygnal API base URL and authentication headers. It also lists support for AI systems connected to databases, APIs, and enterprise files via MCP and tool integration. No named third-party application integrations are publicly listed.