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Solutions
Use-case playbooks for the AI systems teams are already shipping: employee copilots, code agents, customer support, healthcare, legal, financial services, and creative pipelines.
Each page starts from field incidents, then maps the risks to adversarial tests and runtime controls.
Employee copilots, support bots, code agents, and regulated assistants each get controls that fit how they are used.
Findings, citations, escalations, and policy decisions are captured so teams can prove what happened.
Use-case library
The product stack stays the same across deployments: discover the AI surface, red-team realistic attacks, and enforce controls in production. The playbooks change the language, policy checks, and evidence for each workflow.
Secure ChatGPT, Claude Cowork, Microsoft Copilot, Gemini, Slack AI, and other employee copilot apps across prompts, files, browsers, plugins, computer use, and SaaS workflows.
Secure AI coding agents with employee-level traces, command policy, repo-aware evidence, and red-team tests that catch destructive actions before they reach production.
Ground chat, voice, and case assistants in approved policy so refunds, disputes, and disclosures stay accurate under pressure.
Control creative AI and multimodal moderation pipelines with provenance, policy checks, and human review for high-risk outputs.
Keep editorial and campaign AI grounded with source checks, plagiarism controls, and approval evidence before content goes live.
Protect clinical and patient-facing copilots with PHI-safe boundaries, guideline grounding, and escalation controls for unsafe advice.
Secure legal AI with citation validation, matter-scoped retrieval, and guardrails that stop unauthorized advice or privilege leakage.
Protect banking, lending, and insurance AI with approved disclosure language, policy bounds, and evidence for regulated reviews.