Cybersecurity · Application and data security
Secure the payments, customer data and AI workflows your business already depends on. Assessment, implementation and verification—from a single app to an enterprise data platform.
Explore security architectureYou have users, subscriptions or an AI-built product. Identify the gaps that expose customer records, money and account access, then fix them in your existing stack.
Connect application controls with cloud identity, data governance and AI permissions. Turn platform capabilities into policies your team can test, operate and audit.
Interactive security lab
Choose a risk, trigger a synthetic event, then apply safeguards and replay it. See why payments, data and permissions need independent checks.
A polished checkout still trusts a price sent by the browser.
Browser → pricing service
Authorize an order against a trusted catalog. Client values can express intent, but cannot set the money owed. Recheck discounts and currency on the server. This fixture combines price tampering with an unsupported product selection.
The interface says “paid” before a trusted payment event exists.
Return URL → entitlement store
A redirect is navigation, not evidence. Resolve the order server-side and confirm its owner, amount, currency and payment state. Delayed payment methods need pending states. Refunds and disputes need explicit entitlement policies.
An event endpoint trusts a payload and grants access twice.
Payment provider → webhook → records
Provider events may be duplicated or arrive out of order. Authenticate the raw request before parsing business meaning. An event ledger must survive parallel delivery; a read-then-write flag is insufficient. Verify the order as well as the event.
An editable profile field becomes a permission.
Profile → trusted identity → protected API
Authentication identifies a caller; authorization determines what they may do. Hidden controls do not protect APIs. Reject privileged profile fields and evaluate permissions at the action boundary, including background tasks.
A changed record identifier crosses an organization boundary.
Session → tenant → record
Use two ordinary accounts in distinct organizations. Test direct records, lists, exports and writes. Never accept a tenant identifier as proof of membership. Administrative database clients bypass rules and require explicit server checks.
Database rules and upload permissions are separate boundaries.
Anonymous visitor → database and storage
Securing tables does not secure object storage. Review listing, download, upload and signed URL expiration separately. A public client identifier is not an administrator secret; broad policies are the vulnerability in this fixture.
Removing a secret from the browser does not revoke an earlier copy.
Public bundle → privileged service
Inspect built assets and repository history. Keep privileged keys on the server with least privilege. Removing a file is not revocation. Source maps are not intrinsically secret: review their contents rather than claiming every source map is a vulnerability.
A file label and formatted text are accepted without validation.
Upload → server validation → rendering
Browser validation is a convenience. Validate on the server and treat filenames, MIME declarations and generated content as untrusted. A URL-fetch feature also needs separate network restrictions. This fixture does not execute any uploaded content.
A public endpoint has neither request limits nor spending limits.
Public API → workload budget
Rate limits and quotas answer different questions: how quickly and how much. Apply controls before model calls or costly jobs. Distributed deployments require shared counters and a defined failure policy. Do not trust a browser cooldown.
Parallel requests both pass a non-atomic balance check.
API → credit ledger → fulfillment
An idempotency key does not replace balance correctness, and a transaction does not identify a retried logical purchase. Test parallel requests at the boundary with one credit remaining. Commit the ledger and business state consistently.
Untrusted instructions steer a tool toward sensitive data.
External document → agent → authorized tool
A prompt filter cannot guarantee tool authorization. Separate trusted instructions from retrieved content, constrain tool arguments and outbound destinations, and bind approvals to the exact action. This simulation illustrates an export within a scoped workflow that still requires approval.
A release bypasses dependency checks and administrator failures go unnoticed.
Release pipeline → runtime → monitoring
A scanner finding needs context and remediation ownership. An alert is useful only when someone can act on it. Protect logs from secrets and excess personal data. Monitoring detects and supports response; it does not automatically remove every vulnerability.
Review trust boundaries, sensitive flows and configuration.
Prioritized findings, evidence and a practical remediation plan.
Implement the agreed fixes in your current platform.
Reviewed changes and regression tests against the original findings.
Strengthen checkout, entitlements, access policies and private storage.
Verified payment transitions and cross-account access tests.
Make important security events observable and actionable.
Logging boundaries, alert routing and an operational handover.
Xion
Concept · Interactive simulationXion explores a policy layer for AI tools and sensitive workflows. The demonstration shows how a request could be allowed, blocked or held for approval. These are simulated decisions, not a deployed protection service.
Original implementation guidance grounded in official documentation. Publication and revision dates describe the articles, not guarantees about your security.
What Cloudflare Application Profiles add to request protection—and the authorization, payment checks and rollout tests your application still needs.
Read the articlePractical Databricks security guidance for governed data access, AI tool permissions, human approvals and security review evidence.
Read the articleReview server-owned checkout prices, verified webhooks, order ownership and payment entitlements with practical acceptance tests.
Read the articleAI-generated apps can look finished while any signed-in user can read everyone's data. The specific security issues we check before an AI-built app goes in front of customers.
Read the articleAgree on systems, data, payment flows and the access needed for the review.
Record evidence and order remediation by exposure and business impact.
Fix the agreed scope and preserve the behavior customers depend on.
Replay the failures, test legitimate flows, and document ongoing responsibilities.
Yes. Start with its current architecture and the flows that touch money or sensitive data. The assessment establishes what can be fixed in place and which changes need deeper engineering.
No. The same trust boundaries apply to custom SaaS, mobile backends, enterprise platforms and AI workflows.
They provide useful controls. Your access policies, application logic, configuration and response procedures still need implementation and verification.
Xion is a concept project. Its interactive showcase uses local synthetic scenarios and does not monitor or protect your platform.
No. It illustrates common failures without requesting your website, credentials, payment details or customer data.
Bring the features that handle payments, customer data or sensitive actions. We will scope the review and the next practical step.
