The Tool Desk
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Before enabling an AI feature on proprietary chip work, establish exactly what data it can access, where that data goes, who may use it, and what the applicable contract permits. Do not treat a vendor’s statement that you own your designs—or a deployment label such as “cloud,” “BYOC,” or “on-prem”—as a complete confidentiality guarantee. Map the actual workflow, verify the selected product configuration and signed terms, and test its access boundaries with non-sensitive material before production use.
1. Define what the AI feature is allowed to touch
Start with the feature’s real access, not just the text an engineer types into a chat box. An assistant may receive files directly, retrieve context from a source repository or shared document store, invoke EDA tools, or create outputs and logs that persist outside the design environment. Ask the product team or vendor for a precise inventory of inputs, outputs, and automatically collected information for the selected feature and configuration.
- Design inputs: RTL, schematics, layouts, netlists, timing constraints, PDKs, foundry collateral, licensed IP blocks, scripts, and configuration files.
- Related engineering material: source-control history, bug reports, simulation waveforms, tool logs, test results, design reviews, and project documents.
- Generated or intermediate data: prompts and conversation context, embeddings, caches, intermediate artifacts, suggested or modified files, and final outputs.
- Operational information: telemetry, usage records, support bundles, diagnostic data, and information a vendor or subprocessor may see during support.
For each item, record whether the feature can read it, transmit it, store it, or modify it. Ask whether access is limited to selected files or extends to a repository, project, or connected document store. Record which users, services, and administrators can grant or expand that access. This is especially important when a feature can assemble context automatically: engineers may disclose more than they intended simply by connecting a broad repository.
Generated content also needs an owner and a handling rule. Determine whether outputs may contain design details, whether they are saved in the same project or another service, and whether they can be reused as training or evaluation material. Treat a proposed code change, optimization, or design explanation as potentially sensitive until the team classifies it.
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2. Read the applicable terms, not just the ownership statement
Separate ownership from permission to process data. A customer can retain intellectual-property rights while granting a vendor contractual permission to handle the data for specified purposes. Review the agreement incorporated into the order for the exact product, feature, deployment, and geography; do not assume that a public agreement applies unchanged to a different SKU or negotiated contract.
One concrete example is the published Synopsys Cloud Agreement. It says customers own intellectual-property rights in Customer Data, while section 13.3 grants Synopsys, its affiliates, and subcontractors a non-exclusive right to process and use Customer Data to provide Synopsys products and related services, including support, training, and data analytics. The agreement separately treats Usage Data and Feedback as outside the customer’s Confidential Information under that agreement. It also contains liability exclusions and caps. Those are terms of that specific agreement, not a statement about every Synopsys product or every customer’s order.
For your own contract, ask legal and procurement to identify the exact language governing each category below. Obtain written answers where the contract or product documentation is unclear.
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- Rights: who owns inputs, generated outputs, modifications, improvements, and derivative material?
- Processing license: what may the vendor do with customer content, and is that permission limited to providing the service or does it include product improvement, analytics, or model training?
- Separate data categories: how are feedback, prompts, usage data, telemetry, support cases, and de-identified or aggregated information defined and treated?
- Other recipients: which affiliates, infrastructure providers, model providers, and subprocessors may access data, and what obligations bind them?
- Lifecycle and remedies: what are the rules for retention, backup, deletion, export, return, termination, confidentiality exceptions, security-incident notice, audit evidence, and liability?
- Third-party rights: do foundry, PDK, or other IP licenses allow the material to be processed by this service and in the proposed region?
Do not reduce the review to “Does the vendor train on our designs?” Ask what the promise covers, which models and data categories it applies to, whether an opt-out is available, how the choice is enforced, and whether the commitment appears in binding terms. A Siemens EDA AI white paper says customer data is not taken without permission to improve models and that models built from customer data remain under customer control. That is a Siemens-published statement; it does not, by itself, settle the selected feature’s terms, model provider, retention schedule, or subprocessor access.
3. Draw the data-flow boundary for the actual workflow
Make a simple diagram that begins at the engineer’s workstation and follows data through the EDA tool, AI feature, model endpoint, storage, logs, support systems, backups, and any synchronization with on-premises resources. Mark where data is processed and stored, who administers each part, what crosses a network boundary, how long each copy persists, and which identities can access it. Include return paths for generated content and diagnostic data, not only the initial upload.
Ask the vendor to identify the architecture used by the specific AI feature. Synopsys describes customer-managed bring-your-own-cloud (BYOC) and Synopsys-managed SaaS options, as well as a hybrid design that synchronizes data needed and generated in the cloud with on-premises resources. These labels do not reveal every path or access right. Find out whether the AI feature uses a model endpoint separate from the main EDA environment, whether prompts or derived artifacts enter shared services, and whether support personnel can access the relevant systems.
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A useful data-flow record includes:
- Every transfer destination and processing location, including the cloud region and any model or support service.
- The customer, vendor, cloud-provider, and model-provider administrators for each component.
- Encryption boundaries, key-management responsibility, identity controls, and network egress paths.
- Storage, cache, log, backup, and synchronization locations, with their retention and deletion behavior.
- Which data categories are allowed to cross each boundary, and which are blocked by policy.
If the vendor cannot explain a material path or recipient, treat that as an unresolved deployment condition rather than assuming the data stays inside the EDA application.
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Request current documentation and contractual commitments for the controls that matter to your design environment. Confirm not only that a control exists somewhere on a platform, but that it is included, enabled, and configured for the purchased SKU and deployment. Synopsys Cloud pages list SOC 2 Type 2, encryption in transit and at rest, MFA with role-based access, and dedicated virtual networks with workload protection, vulnerability management, and incident response. These are vendor-described platform controls, not an independent comparison of product suitability. Ask for the relevant audit report, its scope and exceptions, and evidence that the selected service is in scope.
| Control area | What to verify |
|---|---|
| Encryption and keys | Encryption in transit and at rest; key ownership, rotation, access, and any customer-managed key option. |
| Identity and isolation | SSO or identity-provider integration, MFA, provisioning and deprovisioning, least privilege, and separation among customers, teams, projects, and repositories. |
| Auditability | Which user and administrator actions are logged, how long logs are retained, whether they can be exported to company monitoring, and how privileged access is reviewed. |
| Network and operations | Network isolation, egress restrictions, secrets handling, endpoint hardening, patching, vulnerability disclosure, and incident-response commitments. |
| Data lifecycle | Retention periods, backup copies, deletion evidence, export or return options, and what happens to data after subscription termination. |
For each answer, distinguish between a feature description, a configuration setting your administrators must enable, and a contractual promise. A security page can describe platform capabilities without establishing that they are enabled in your tenant or that a specific retention or incident-notice deadline is binding. Have the security reviewer map each control to the design’s sensitivity and the company’s existing policy.
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5. Put explicit limits on AI agents and generated changes
An agent that can invoke tools or work across a repository has a different risk profile from an assistant that only answers questions. Define its authority in advance: which folders and files it may read, which files it may change, which EDA tools and APIs it may call, which commands it may execute, and which network destinations it may contact. Begin with read-only permissions and a narrow, non-sensitive project.
Set approval and recovery rules before allowing write or execution privileges. Require human approval for consequential edits, tool runs, or data exports; log prompts and tool calls in a protected location; maintain rollback or version-control recovery; and require human signoff before a generated change enters a production flow. Validate results through established simulation, verification, and signoff processes rather than treating an agent’s explanation as proof of correctness.
Siemens’ announcement for Fuse EDA AI Agent describes role-based access controls, audit trails, and human checkpoints. These are product claims to validate against the deployed version and configuration. Siemens’ July 29, 2026 blog also characterizes generic LLMs as lacking domain-specific knowledge and being poorly suited to security-sensitive design work; that is the author’s vendor-published assessment, not an independent comparative test. For your own acceptance test, use a non-sensitive design to check whether the agent can reach forbidden files, exceed its role, transmit data unexpectedly, or make an unapproved change. Confirm that logs capture actions and that rollback works.
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6. Compare deployment options by controls, not labels
There is no universal rule that cloud, BYOC, or on-premises deployment is safest. Compare the actual options against the same requirements and your threat model. Customer-managed infrastructure may change who controls accounts or networks without removing the vendor’s access, model calls, or data-processing permissions. Conversely, a managed service may provide controls that are difficult for a team to operate itself. The choice depends on architecture, contract, licensing, staffing, and policy.
| Decision criterion | Questions for each option |
|---|---|
| Residency and movement | Where are inputs, outputs, logs, backups, and model calls processed? What synchronization or cross-region transfers occur? |
| Administrative responsibility | Who controls the cloud account, keys, operating system, network, identity configuration, and logs? Which tasks remain the customer’s responsibility? |
| Provider access | Can the vendor, cloud provider, model provider, or subprocessors access data or systems, including for support? |
| Enforceable commitments | Do signed terms cover permitted use, training, retention, deletion, incident notice, and the relevant data categories? |
| Company integration | Can the option use company identity, access reviews, monitoring, and incident-response processes? |
| Operational fit | Can the team meet the option’s administration, scaling, performance, cost, and support demands without weakening controls? |
Synopsys documents BYOC and managed SaaS choices; Siemens describes EDA AI deployment options and customer-controlled data approaches. Compare the exact offered configurations rather than assuming that products with similar labels provide equivalent boundaries. Include the operational burden in the decision: a theoretically restrictive design is not protective if the team cannot reliably patch it, monitor access, or respond to incidents.
7. Use a staged approval before production
A short, recorded approval process gives engineering, security, legal, and procurement a shared basis for enabling a feature. Keep the decision specific to a product version, feature, deployment, data class, and intended workflow; a broad approval for “AI in EDA” may not capture meaningful differences between tools or configurations.
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- Obtain product-specific answers. Request the architecture and data-flow details, subprocessor and model-provider information, retention behavior, available controls, and current security evidence for the exact configuration.
- Resolve contractual questions. Have legal and procurement compare the applicable order and service terms with company requirements for processing, training, confidentiality, deletion, audit, incident notice, and liability.
- Configure least privilege. Connect only the necessary project context, set roles and network restrictions, disable unneeded integrations, and establish monitoring and log retention.
- Test with non-sensitive designs. Exercise expected workflows and failure cases, verify permission boundaries and logs, check generated changes through normal engineering validation, and confirm rollback.
- Record approval and review triggers. Document allowed uses, excluded data, owners, exceptions, and the conditions that require reassessment—such as a new model provider, feature, data flow, contract, or deployment.
If any critical question remains unanswered—especially whether the feature can access a sensitive source, where data is sent, or how it may be reused—limit the feature to data approved for that uncertainty or defer production enablement. Record who owns the decision and what evidence would close the gap; do not infer a safe boundary from a product name or marketing description.
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