Before enabling an AI feature on proprietary chip designs, map every input, output, and data transfer; verify the selected product’s contract and configuration; and limit the feature’s access until security and engineering teams have tested it. A “cloud,” “BYOC,” or “on-prem” label alone does not establish where design data goes or who can access it.
1. Inventory everything the AI feature could receive or create
Start with the feature’s actual context, not just the prompt an engineer types. An assistant connected to a repository, project workspace, or shared document store may access material beyond the file an engineer intended to discuss. Ask the vendor and internal administrators to identify every source the feature can read, including implicit context and data gathered by integrations.
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Include design inputs and outputs, as well as the supporting material that can reveal design intent or enable reconstruction. Depending on the workflow, this can include:
- RTL, schematics, layouts, netlists, timing constraints, PDKs, foundry collateral, and licensed IP blocks.
- Source-control history, scripts, bug reports, simulation waveforms, tool logs, results, and design metadata.
- Prompts, model context, embeddings, caches, intermediate artifacts, generated code or design changes, and other derived outputs.
- Telemetry, support bundles, error reports, and copies made for backups or troubleshooting.
For each item, record whether the feature can view, store, transform, or transmit it; whether an engineer can remove it from the context; and whether it remains available after a session ends. Track generated material too: a proposed change or analysis may itself disclose confidential design information.
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The scope of a contract’s data definition matters. For example, the published Synopsys Cloud agreement defines Customer Data broadly, covering content, materials, designs, data, and information entered, uploaded, or developed through use of a product or portal, including modifications and derivative works. That is a term in that particular agreement, not a definition to assume for other products or orders.
2. Establish exactly what the contract permits
Marketing statements such as “you own your data” do not, by themselves, explain what the provider may do with it. Ownership and permission to process are separate questions. Review the agreement incorporated into the specific order, along with any service-specific terms, data-processing terms, and relevant security documents. Record the answer to each of these questions in writing:
- Who owns customer inputs, generated outputs, and any improvements or derivative material?
- What license does the provider receive to process customer content, and is it limited to delivering the service or does it also cover support, analytics, product improvement, or model training?
- Are feedback, prompts, usage data, telemetry, support cases, or de-identified or aggregated information treated differently from confidential customer content?
- Which affiliates, infrastructure providers, model providers, and other subprocessors may access data, for what purpose, and under what confidentiality and security obligations?
- What are the retention, backup, deletion, export, return, and termination procedures? Do they cover copies and derived data as well as primary storage?
- What confidentiality exceptions, incident-notification duties, audit evidence, and liability limits apply?
- Do foundry and third-party IP licenses permit sending the relevant material to this service and region?
The published Synopsys Cloud agreement illustrates why the full wording matters. It says the customer owns intellectual-property rights in Customer Data, while section 13.3 grants Synopsys, its affiliates, and subcontractors a non-exclusive right to process and use that data to provide Synopsys products and related services, including support, training, and data analytics. The agreement also treats Usage Data and Feedback as not being the customer’s Confidential Information under that agreement, and includes liability exclusions and caps. These are terms of that published agreement; they should not be generalized to every Synopsys product or assumed to govern a reader’s order. Check the agreement actually incorporated into the purchase and have legal staff assess the permitted uses and exceptions.
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Ask separately whether customer content is used to train or improve models. Siemens’ EDA AI white paper states that customer data is not taken without permission to improve models and that models built from customer data remain under customer control. This is a Siemens-published statement, not a substitute for confirming the selected feature’s contract, the model provider involved, retention schedule, and subprocessor arrangements. A useful answer identifies the exact feature and data category, states whether use is opt-in or opt-out, and explains how the choice is enforced.
3. Draw the data-flow map for the selected workflow
Trace the path from the engineer’s workstation through the EDA application, AI service, model endpoint, storage, logs, support systems, backups, and any synchronization with on-premises resources. For each step, note the data involved, its location, the organization or role administering it, the encryption boundary, retention period, and permitted access. Include outbound network calls and operational access by provider personnel, not only the primary storage location.
Synopsys describes both a customer-managed BYOC environment and a Synopsys-managed SaaS environment. Its hybrid description says data needed and generated in the cloud synchronizes with on-premises resources. These labels describe deployment patterns, not the complete boundary for a particular AI feature. Confirm which architecture that feature uses, where model inference occurs, whether prompts or outputs are stored, and how synchronization handles intermediate files, logs, and backups.
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A practical data-flow record can be a diagram or a table. It should let a security reviewer follow a particular design artifact from its source to its destinations and establish who can access each copy. If the provider cannot explain a transfer path or identify the relevant service and subprocessors, treat that uncertainty as unresolved rather than inferring that the data stays local.
4. Verify safeguards against the actual use case
Request current documentation and, where needed, written contractual commitments for controls that matter to the design’s sensitivity. A platform-level capability does not prove it is included, configured, or contractually committed for the selected SKU and deployment.
- Encryption and keys: Confirm encryption in transit and at rest, how keys are managed, who controls them, and whether customer-managed keys are available for this configuration.
- Identity and separation: Check SSO, MFA, provisioning and deprovisioning, least-privilege roles, and boundaries between users, projects, teams, and customers.
- Auditability: Establish which user and system actions are logged, how long logs are retained, whether they can be exported to internal monitoring, and how privileged administrator access is recorded and reviewed.
- Network and endpoints: Ask about network isolation, egress controls, secrets handling, endpoint hardening, patching, vulnerability disclosure, and incident response.
- Retention and deletion: Confirm storage periods for prompts, source files, outputs, logs, and backups; how deletion is performed and evidenced; and what happens after subscription termination.
- Model-use controls: Determine whether design data can be excluded from model training or service improvement and how that exclusion is applied to the feature and any subprocessors.
Synopsys lists 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 on its Cloud platform pages. These are vendor-described platform controls, not an independent determination that a particular configuration is suitable for a particular design. Request the audit report applicable to the service, its scope and exceptions, and evidence that the feature and deployment in the order are covered. Confirm which controls are operational defaults, which require configuration, and which are binding commitments.
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5. Put explicit limits on AI agents
An agent that can work across repositories or invoke EDA tools may read or change files, execute commands, call APIs, or transmit information. Treat those capabilities as authority to act, not merely a conversational interface. Before production use, specify:
- Which folders, repositories, design projects, tools, APIs, commands, and network destinations it may access.
- Whether permissions are read-only by default, and what approval is required before the agent writes, executes, or exports anything.
- How prompts, tool calls, approvals, file changes, and data transfers are logged, who can review those records, and how records are protected.
- How to stop a run, roll back changes, restore known-good files, and identify the human who approved a consequential action.
- Which simulation, verification, and signoff flows must independently validate generated or modified results.
Begin with least privilege and a non-sensitive design. Test both allowed and disallowed actions: for example, whether the agent refuses access to a prohibited folder, requires approval before a write or execution, and leaves a useful audit trail. Confirm that permissions hold across integrations and failure modes, not just in a successful demonstration. Siemens’ Fuse EDA AI Agent announcement describes role-based access controls, audit trails, and human checkpoints in that product. Those are product claims to validate against the deployed version, not a guarantee about every agent or configuration. Siemens also discusses risks in generic agent systems, including inadequate access control and accidental exposure through internet or cloud resources.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsGenerated suggestions should remain proposals until established engineering checks validate them. An agent’s explanation is not a replacement for simulation, formal verification, review, or signoff appropriate to the design and change.
6. Compare deployment options using one set of criteria
Do not decide from a label alone. Compare available options against the same requirements, including the AI feature’s model endpoint and operational workflow—not only where the main EDA application runs.
| Criterion | Questions to answer |
|---|---|
| Residency and movement | Where are prompts, design files, outputs, logs, backups, and model calls processed or stored? What transfers between cloud and on-premises systems? |
| Administrative control | Who controls the cloud account, network, operating system, keys, storage, and logs? Which responsibilities stay with the customer? |
| Provider access | Can the vendor, cloud provider, model provider, or support personnel access content? Under what circumstances and controls? |
| Enforceable commitments | What do the order and service terms say about permitted data use, training, retention, deletion, incident notice, and confidentiality? |
| Security integration | Can the deployment integrate with company identity, access reviews, monitoring, audit export, and incident response? |
| Operational fit | What staffing, maintenance, support, scaling, performance, and cost responsibilities follow from the option? |
| Policy and threat model | Does the arrangement meet internal rules, foundry or third-party licenses, residency needs, and the design’s sensitivity? |
Synopsys documents BYOC and managed SaaS choices, while Siemens describes EDA AI deployment options and customer-controlled data approaches. The available documentation does not establish a universal security ranking among cloud, customer-managed, hybrid, or on-premises deployments. The relevant choice depends on the actual data paths, access model, controls, contract, licenses, operational capacity, and threat model. A customer-managed environment can still expose data through model calls, logging, or misconfigured access; a managed service can offer controls that still need to be verified for the selected configuration.
7. Use a gated pilot before enabling production designs
Turn the checks into a decision record shared by engineering, CAD/IT, security, procurement, and legal. Keep the pilot separate from production repositories and use non-sensitive material until the relevant controls and permissions have been tested.
- Define scope: Name the AI feature, product edition, deployment, users, data types, integrations, model provider, and intended task. Exclude unrelated tools or services from the approval.
- Approve the data map and terms: Document inputs, derived data, outputs, destinations, access roles, retention, and deletion. Resolve contractual permissions, subprocessor access, licensing constraints, and model-use terms for the actual order.
- Configure protections: Apply identity controls, least privilege, network restrictions, logging, retention settings, and any available training or improvement exclusions. Record who owns each configuration and how it will be checked.
- Test with safe designs: Exercise normal tasks and boundary cases, including prohibited files, attempted writes, command execution, exports, logging, and deletion. Confirm that the agent cannot exceed its approved authority.
- Validate engineering outputs: Run generated or changed material through the established review, simulation, verification, and signoff process before relying on it.
- Record approval and review triggers: Identify accountable owners, approved data classes, deployment settings, and conditions that require reassessment—such as a changed model provider, feature, contract, region, or permission set.
Do not approve production use if the team cannot establish what data the feature can access, where it goes, who may process it, what uses are contractually allowed, or how agent actions are contained and audited. Narrowing the data scope or using read-only access may resolve a specific gap; an unresolved material contract or data-flow question calls for withholding the affected designs until it is answered.
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