Solutions

Find the use case that matches the risk in front of you.

Tutela by H2H helps teams understand where sensitive data, AI activity, and access paths create risk, then decide what needs action first.

AI Enablement Journeys

Show customers how safe AI adoption works in the jobs employees already do.

These journeys translate shadow AI risk into governed operating paths: employee goal, uncontrolled data movement, policy moment, product fit, safe outcome, and proof.

Featured journey

Finance AI in Excel

Govern AI-assisted spreadsheet work by inspecting prompts, files, outputs, model routing, and sensitive-data context before finance workflows become shadow AI.

Employee goal: A finance analyst wants ChatGPT or an AI assistant to generate formulas, variance analysis, charts, and executive summaries for a quarterly forecast.

Governance moment: Agentic Security inspects prompts, files, generated outputs, and model-routing decisions while Data Security helps determine whether the spreadsheet contains regulated, payroll, M&A, customer, or confidential revenue data.

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Featured journey

Developer AI Agent Governance

Enable local AI coding agents with governance for repo access, secrets, terminal actions, generated code, dependencies, infrastructure edits, and reviewable evidence.

Employee goal: An engineer wants to run Codex-style agents locally to understand architecture, refactor code, execute commands, and update infrastructure templates faster.

Governance moment: Agentic Security governs file, prompt, terminal, and tool-call context while Data Security helps identify source-adjacent secrets, customer samples, and sensitive data that should influence policy.

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Legal Contract AI Review

Govern AI-assisted contract summarization by inspecting documents, prompts, outputs, model routing, and sensitive contract context.

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Security Analyst AI Investigation

Govern AI-assisted alert investigation with visibility into prompts, tool calls, sensitive data, usage signals, policy actions, and analyst-ready evidence.

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Connected Product Management AI

Enable AI connected to Jira, Slack, Salesforce, and internal docs with policy, sensitive-data context, model interaction review, and audit evidence.

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Use cases

Start with the security question your team is trying to answer.

Each use case maps to a concrete assessment: where sensitive data lives, how AI is using it, which access paths matter, and which findings deserve remediation.

Sensitive Data Discovery

See where sensitive data lives, understand who can reach it, and prepare better protection decisions with the right data context.

Best for: Security teams, data owners, and cloud teams trying to understand where sensitive data lives and what deserves attention first.

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Agentic Workflow Governance

Review agent access, prompt and output risk, and governance expectations before agentic workflows scale.

Best for: Security, AI, and platform teams governing employee AI use, copilots, agents, and model interactions around protected data.

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Access Risk Review

Connect identity, permissions, and sensitive data context so access review becomes easier to prioritize and explain.

Best for: Security and identity teams investigating where access paths create risk across sensitive data environments.

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Secure Employee AI Use

Understand employee AI activity, inspect sensitive interactions, and apply policy before everyday AI use becomes unmanaged risk.

Best for: Security, IT, and AI teams deciding how employees can use copilots and AI assistants without creating new data risk.

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Problem framing

Problem areas

Sensitive data risk

Start by understanding where regulated, confidential, and business-critical data lives and why it deserves attention first.

Access and control context

Understand who can reach protected information, how relationships expand exposure, and which access questions deserve a deeper look.

Employee AI governance

Understand how employee AI use changes the risk surface when prompts, files, outputs, and policy controls meet protected data.

Operating contexts

Where Tutela by H2H fits.

These are common security operating situations where teams need evidence about data movement, AI activity, access paths, and remediation ownership before the next decision.

Application security teams

AppSec teams are asked to reduce application risk while data moves through cloud services, APIs, SaaS exports, and internal workflows. Tutela by H2H helps identify sensitive-data exposure, connect it to access paths, and give remediation owners evidence they can act on.

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AI-native product teams

AI-native teams need to know when prompts, files, agents, model outputs, or connected tools touch protected data. Tutela by H2H gives them a way to inspect AI activity, apply policy decisions, and preserve an audit trail without moving control out of their environment.

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Data-heavy SaaS companies

SaaS and digital product companies accumulate customer data, product telemetry, support exports, and employee AI workflows faster than manual processes can keep up. Tutela by H2H helps them classify sensitive data, prioritize exposure, and prepare stronger customer security conversations.

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Cloud and platform security teams

Cloud and platform security teams need to understand where storage, identity, application access, and posture findings overlap. Tutela by H2H keeps the sensitive-data map, access context, and operating evidence inside a customer-owned deployment model.

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Lean security teams

Lean security teams still have to answer high-stakes questions about sensitive data, AI use, access risk, and remediation. Tutela by H2H helps focus limited time on the data and exposure paths that create the most practical risk.

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Regulated data teams

Teams handling regulated or customer-sensitive data need evidence for access governance, AI policy, vendor diligence, and compliance conversations. Tutela by H2H helps organize data risk, policy decisions, and closure evidence in a form stakeholders can inspect.

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Industries

Industry

Financial Services

Financial-services buyers need to protect customer data and operating discipline while proving they can govern access, AI usage, and sensitive data review without relying on vague product promises.

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Industry

Healthcare

Healthcare organizations need to protect regulated data while making careful choices about access, AI, and deployment fit. That evaluation should feel disciplined, not improvised.

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Industry

Technology and SaaS

Technology and SaaS companies move quickly, but customer data, product telemetry, internal tools, and employee AI workflows can create exposure faster than review processes can keep up.

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Industry

Public Sector

Public-sector teams need to balance modernization, sensitive-record handling, procurement discipline, and AI adoption without turning security review into guesswork.

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