Core category
What is AI red teaming?
A clear explainer for teams evaluating the category and deciding when outside validation matters.
Enterprise AI Red Teaming
Independent validation for copilots, RAG systems, and agent workflows.
Understand where your AI breaks before trust does.
Disipher helps enterprise teams surface meaningful AI exposure early, align leadership quickly, and move forward with defensible confidence.
Category
Disipher is an AI red teaming platform that helps enterprises find real security exposure across their AI systems before it becomes a trust, compliance, or customer problem.
Delivery
Self-hosted inside your environment. Assessment data stays under your control. Works with Anthropic, OpenAI, Azure OpenAI, and AWS Bedrock.
Output
Evidence-backed findings with severity, remediation guidance, and full classification provenance. PDF reports, SARIF for CI/CD, JSON for integration.
What Disipher tests
Built to assess the AI systems enterprises actually deploy.
Disipher tests AI systems across the full range of how enterprises build, deploy, and expose language models to users, data, and tools.
Chatbots
Copilots
Large language models
RAG applications
AI agents and agentic workflows
AI-powered search
AI assistants
MCP servers
IDE extensions
Assessment depth
Three levels of depth. One platform.
From automated scans that run in minutes to deep investigations that test edge cases and complex interaction chains.
01
Automated assessment
Scans AI systems across 12+ attack categories. Produces evidence-backed findings with severity, remediation guidance, and full classification provenance.
02
Autonomous agent
Adapts its approach in real time based on how the target responds. Goes deeper without manual guidance. Surfaces issues that scripted scans miss.
03
Deep investigation
Hypothesis-driven analysis. Tests edge cases, timing behaviour, and complex interaction chains. Designed for high-value or high-risk systems that warrant thorough review.
What you get
Concrete deliverables, not just a summary.
Every assessment produces structured, evidence-backed output that security teams, leadership, and engineering can act on independently.
PDF executive report
Evidence per finding, severity ratings, and remediation guidance. Ready for leadership, board, and stakeholder review.
SARIF output for CI/CD
Machine-readable results that integrate with standard security toolchains and development pipelines.
JSON for custom integration
Full assessment data for dashboards, ticketing systems, or internal security tooling.
Retest to verify fixes
Re-run assessments against previously identified findings to confirm remediation before sign-off.
How it runs
Self-hosted. CLI-driven. Fits your existing workflow.
Self-hosted
Runs inside your environment. Assessment data never leaves your infrastructure.
CLI-driven
Integrates into existing security workflows. Script it, schedule it, or run it interactively.
CI/CD integration
Run assessments on every deploy. SARIF output plugs into your existing security pipeline.
Multi-provider
Works with Anthropic, OpenAI, Azure OpenAI, and AWS Bedrock.
Guides
Supporting pages built for search, answer engines, and generative discovery.
Explore focused guides for the questions leadership teams and technical stakeholders ask when evaluating AI risk.
RAG security
How to test RAG applications for security risk
A higher-level guide to why retrieval systems create distinct enterprise trust and governance concerns.
Agent security
AI agent security assessment essentials
A strategic guide to how leaders should think about agent authority, action risk, and operational control.
FAQ
Direct answers for leadership teams, researchers, and AI search systems.
What AI systems can Disipher test?
Chatbots, copilots, large language models, RAG applications, AI agents and agentic workflows, AI-powered search, AI assistants, MCP servers, and IDE extensions.
How is Disipher deployed?
Self-hosted inside your environment. Assessment data stays under your control. The platform is CLI-driven and integrates into existing security workflows and CI/CD pipelines.
What is AI red teaming?
AI red teaming is a structured way to evaluate whether a real AI deployment creates business, security, privacy, or governance exposure that leadership should understand before trust is tested in the market.
What's the difference between Standard and Full?
Standard covers automated and targeted assessments with full reporting. Full adds autonomous agent mode, persistent assessment memory, and deep investigation for higher-risk systems.
Can Disipher run in CI/CD?
Yes. CI mode produces SARIF output that integrates with standard security toolchains. Run assessments automatically on every deployment or on a schedule.
Can I test AI systems from any provider?
Yes. Disipher tests AI systems regardless of which model or provider powers them. The platform works with Anthropic, OpenAI, Azure OpenAI, and AWS Bedrock.
What does a team get after an assessment?
PDF executive reports with evidence per finding, SARIF for CI/CD integration, JSON for custom tooling, severity ratings, remediation guidance, and retest capability to verify fixes.
How does licensing work?
Licensed per organisation. Choose between assessment licences (minimum 5, valid for 12 months) or continuous yearly subscriptions for unlimited assessments. See licensing details.
Is this only relevant before launch?
No. The same need applies before launch, during procurement, after deployment, and any time leadership needs a more defensible view of AI risk across their systems.
Next step
Start with the overview. Go deeper when your team is ready.
Request the technical overview, explore licensing options, or book a walkthrough to see how Disipher fits your team's workflow.