Compliance
Harvey in European Union
Structured issue-spotting for deploying Harvey in European Union, read against the rules for that jurisdiction.
- Source
- Rules engine over a generic brief — no anchored quote on this page
- Verified
- Evidence not verified
- Confidence
- Low
Structured issue-spotting to support your own review — not legal advice. Verify against the cited primary sources and your counsel.
01What this reading assumes
- Jurisdiction
- European Union
- Principal framework
- Regulation (EU) 2016/679 (GDPR). Directly applicable in every Member State and unamended as of the review date. An AI deployment engages Articles 5 and 6 on principles and lawful basis, 9 on special categories, 13 and 14 on information, 22 on decisions based solely on automated processing, 28 on processors, 32 on security, 35 on impact assessments and Chapter V on transfers out of the EEA. Member States retain room to legislate on employment, which is where Germany’s BDSG § 26 comes in.
- Delivery assessed
- Enterprise SaaS
- Data leaves the network
- unknown — the deciding question for a hosted product
- Vendor home jurisdiction
- the United States
- Verified vendor positions
- none — every vendor position below is a question, not an assurance
- Rules evaluated
- 33
- Rules fired
- 23
Assumptions about use
- An internal deployment used by employees, not a public-facing product.
- A person reads the output before acting on it — but that is not recorded, so the engine reports it as a gap rather than assuming it.
- No significant automated decision is taken about a person by the system alone.
02Issues to work through
Cross Cutting
Confidentiality duties bind independently of data protection law
Material can be entirely free of personal data and still be the material a contract stops you disclosing. Client retainers, non-disclosure agreements, supplier contracts and common-law duties are the usual sources, and several of them require consent before a third party processes the material at all — which a model API call is.
- Required checks
- Review the confidentiality clauses in the contracts covering the material going into the system.
- Identify any contract requiring notice or consent before a subcontractor processes the material.
- Decide whether the deployment needs a confidentiality carve-out negotiated into new contracts.
- Vendor questions
- Will the vendor accept a confidentiality undertaking beyond its standard terms?
- Which staff at the vendor can access customer content, under what controls?
- Technical controls
- Segregate the most sensitive corpora into an index that the general assistant cannot reach.
Human review required — take this to your counsel
What ends up in a prompt, and where it goes next
Every prompt is a transfer of whatever it contains. Staff paste more than they intend, retrieved context travels with the prompt, and system prompts can often be extracted from the output. Assume anything reaching the model has left your control unless the contract and the architecture say otherwise.
- Required checks
- Write down which categories of information may be entered into a prompt, and tell people.
- Establish what the system prompt contains and whether disclosing it would matter.
- Establish which shadow tools staff are already using; the policy has to name the permitted ones.
- Vendor questions
- Are prompts and completions retained, for how long, and can retention be set to zero?
- Are prompts used for abuse monitoring, and if so who can read them and for how long?
- Technical controls
- Redact or block high-risk patterns before the prompt leaves the application.
- Keep prompt and completion logs out of general-purpose observability tools.
- Set an explicit retention period on prompt logs and enforce it.
The acceptable-use policy may exclude your use case
Acceptable-use policies commonly carve out unsupervised legal, medical and financial advice, decisions about people without human review, and some surveillance and biometric uses. They are incorporated into the contract by reference and change without a signature, so the version that matters is the one live on the day you rely on it.
- Required checks
- Read the acceptable-use policy against your actual use case, not against a summary of it.
- Where a carve-out applies, decide whether human review brings the use back inside the policy.
- Set a reminder to re-read the policy — it changes without notice to you.
- Vendor questions
- Does your acceptable-use policy permit this use case, and will you confirm that in writing?
- How are we notified when the acceptable-use policy changes?
Human review required — take this to your counsel
Being able to reconstruct a decision months later
The question that arrives after a complaint is what the system was shown and what it produced on a particular day. Models change, prompts change, and indexes are rebuilt, so the answer has to be recorded at the time. Without it, the only available response is that the output cannot be reproduced.
- Required checks
- Decide what is recorded per interaction: model and version, prompt template version, retrieved document ids, output, reviewer and outcome.
- Set how long those records are kept, balanced against the retention duties that also apply to them.
- Vendor questions
- Does the vendor pin model versions, and how much notice is given before a model is retired or changed?
- Technical controls
- Version prompt templates in source control and log the version used.
- Log the model identifier and version returned by the provider, not the one you requested.
Vendor documentation has not been verified
We could not verify a data processing agreement, a subprocessor list, a position on training on customer data and a stated processing region for this vendor from a retrieved document. That is a gap in our evidence, not a finding against the vendor: until a document has been fetched and read, nothing here should be treated as settled either way.
- Required checks
- Obtain the current versions of the processing agreement, subprocessor list, security page and any regional-processing commitment.
- Check that what the sales conversation promised also appears in the contract.
- Vendor questions
- Where is your data processing agreement published, and which version applies to us?
- Where is your subprocessor list, and how much notice do we get before it changes?
- Do you train on customer content by default, and where is that stated contractually?
- In which country or region is inference performed, and where are logs retained?
We were not told whether a person reviews the output
Where output influences a decision about a person, the reviewer has to be able to disagree with it. That needs three things a rubber-stamp review lacks: enough information to judge, enough time to judge, and an override that is used often enough to be real. Design it before the volume makes it impossible.
- Required checks
- Name the role that reviews the output and what they see when they do.
- Decide what evidence is retained about each review, so the practice can be shown to exist.
- Set a threshold below which the system must not act without review.
- Vendor questions
- Does the product expose the retrieved context and the confidence behind a suggestion, or only the answer?
- Technical controls
- Show the reviewer the retrieved sources next to the suggestion, not the suggestion alone.
- Record the reviewer’s decision, including overrides, as part of the audit trail.
Human review required — take this to your counsel
An AI deployment creates new copies of the data
Vector indexes, prompt logs, completion caches, evaluation datasets, fine-tuning checkpoints and backups are all copies of the source material in places the existing retention schedule does not mention. Deletion requests are the moment this is discovered, because deleting the source document does not delete its embedding.
- Required checks
- List every store the deployment creates and add each to the retention schedule.
- Establish how a deletion request propagates to the index, the caches and the logs.
- Establish how long backups keep material that has been deleted from the live system.
- Vendor questions
- What does the vendor retain, where, and for how long after we delete our copy?
- Technical controls
- Store the source document id with every embedding so deletion can cascade.
- Set time-to-live on prompt and completion logs rather than relying on manual cleanup.
The vendor’s terms may not permit the deployment you are planning
Provider terms routinely restrict things architectures assume: sharing seats, building a competing service, benchmarking and publishing results, reselling capacity, and processing certain data categories. A consumer or self-serve plan often carries different terms from the enterprise agreement, and the enterprise agreement is the one worth reading.
- Required checks
- Identify which contract actually governs — self-serve terms, an order form, or a negotiated agreement.
- Check restrictions on seat sharing and on service accounts, which a shared internal assistant can breach without anyone noticing.
- Check whether the terms allow the categories of data you intend to send.
- Vendor questions
- Which agreement governs our use, and can we have the current version in writing?
- Are there restrictions on the data categories or the industries we may use the service for?
European Union
Article 35 — a data protection impact assessment is likely required
Where a type of processing, in particular using new technologies, is likely to result in a high risk to the rights and freedoms of natural persons, the controller must carry out an impact assessment before the processing. Systematic evaluation of people, large-scale special category processing and systematic monitoring of a publicly accessible area on a large scale each point that way, and an AI deployment over employee or customer data usually touches at least one.
- Required checks
- Run the assessment before deployment, not after, and keep it as a living document.
- Include the residual risk and the decision to accept it, signed by someone who can accept it.
- Check the national supervisory authority’s list of processing that always requires an assessment.
- Vendor questions
- Will you provide the technical information we need to complete an impact assessment?
Human review required — take this to your counsel
Chapter V — the transfer is often the inference call
Transfers of personal data to a third country need an Article 45 adequacy decision, Article 46 appropriate safeguards, or a derogation. In an AI deployment the transfer is easy to miss because it is not the storage contract: it is the model API call, the log pipeline and the support tunnel, and those three can end up in three different countries.
- Required checks
- Identify every hop where personal data leaves the EEA, including logging and support access.
- For each, identify the Article 45 or 46 route relied on.
- Complete a transfer impact assessment where safeguards rather than adequacy are relied on.
- Vendor questions
- Where is inference performed, and can it be pinned to an EEA region contractually?
- Which of your staff outside the EEA can access customer content, and under what controls?
- Technical controls
- Pin regional endpoints in configuration and alert on traffic served from elsewhere.
Human review required — take this to your counsel
Article 28 — the processor contract and the guarantees behind it
A controller may use only processors providing sufficient guarantees to implement appropriate technical and organisational measures. The contract must be in writing, including in electronic form, and must contain the Article 28(3) terms, including that the processor acts only on documented instructions. A published security page is not a guarantee; a contract is.
- Required checks
- Obtain the processing agreement and check it against the Article 28(3) list, term by term.
- Confirm the documented instructions actually describe what you want done.
- Check the audit and assistance provisions are usable rather than theoretical.
- Vendor questions
- Which entity is the processor, and does the agreement cover the model provider behind you?
- How do you support us in responding to data subject requests?
Annex III point 4 — employment and worker management is a high-risk class
Annex III point 4 covers AI intended to be used for the recruitment or selection of natural persons — including placing targeted job advertisements, analysing and filtering applications, and evaluating candidates — and for decisions on terms of work, promotion, termination, task allocation and performance monitoring. The high-risk obligations in Chapter III Sections 1 to 3 apply from 2 December 2027, moved from 2 August 2026 by Regulation (EU) 2026/1744. The Annex III text itself did not change.
- Required checks
- Confirm whether the intended use falls in Annex III point 4, and record the reasoning either way.
- Identify whether you are a provider or a deployer; buying a tool usually makes you a deployer, but rebranding or materially modifying it can make you a provider.
- Plan against the 2 December 2027 date rather than treating the deferral as a reprieve — a system procured now will still be running then.
- Vendor questions
- Will you classify this system under the AI Act, and will you provide the instructions for use a deployer needs?
- What is your timeline for conformity assessment and the EU declaration of conformity?
- Technical controls
- Build automatic logging now: the deployer obligations include keeping logs, and retrofitting them is expensive.
Human review required — take this to your counsel
Deployer duties: oversight by people with competence and authority
For high-risk systems the AI Act puts duties on the deployer as well as the provider: use the system according to the instructions for use, assign human oversight to people with the necessary competence, training and authority, ensure input data is relevant and sufficiently representative, and inform workers and their representatives before putting the system into use. These apply from 2 December 2027 for Annex III systems.
- Required checks
- Name the people who will exercise oversight and confirm they have authority to override.
- Confirm the input data you will feed the system is relevant to its intended purpose.
- Plan the notification to workers and their representatives.
- Vendor questions
- Can we see the instructions for use, including the stated limitations and the expected oversight measures?
- Technical controls
- Surface the system’s confidence and the basis for an output so oversight is informed.
Human review required — take this to your counsel
Article 22 — decisions based solely on automated processing
Article 22 gives a data subject the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects or similarly significantly affects them. Where the contract or consent exception applies, the controller must implement suitable measures to safeguard the data subject’s rights including at least the right to obtain human intervention, to express a point of view and to contest the decision. Automated shortlisting is the archetype.
- Required checks
- Decide whether the decision is "solely" automated, and be honest about whether the human step is real.
- If it is, identify which Article 22(2) exception applies.
- Build the contest route: who hears it, on what information, and within what time.
- Vendor questions
- Does the product support a human review step that shows the reviewer why the output was produced?
- Technical controls
- Record every decision with its inputs, so a contest can be answered with facts.
- Make the override path a first-class feature rather than an out-of-band email.
Human review required — take this to your counsel
Data governance and measured outcomes, not asserted fairness
The AI Act’s data governance requirements for high-risk systems cover the relevance, representativeness and error profile of training, validation and testing data, and the examination of possible biases. The GDPR accuracy principle points the same way. Both are answered with measurement and a record, not with a statement that the vendor tested for bias.
- Required checks
- Define what a fairness failure would look like for this use case, before measuring.
- Establish a baseline against the process the system replaces, so a comparison is possible.
- Set a review cadence and a threshold that triggers action.
- Vendor questions
- What bias evaluation have you performed, on what data, and will you share the results?
- Technical controls
- Log outcomes with enough structure to analyse them by group, subject to the data protection constraints on doing so.
Whether your content trains the model, and what the EDPB says about that
EDPB Opinion 28/2024 addresses when an AI model trained on personal data can be considered anonymous — its position is that this cannot be assumed and must be assessed case by case — and when legitimate interest can support developing or deploying such a model. For a buyer the practical question is narrower: does the contract say your content is not used for training, and for how long is it retained.
- Required checks
- Find the training commitment in the contract, not in the marketing page.
- Establish whether abuse monitoring creates a separate retention path with different rules.
- Vendor questions
- Is customer content used to train or improve models by default, and where is that stated contractually?
- If we opt out of training, what is retained, for how long, and who can read it?
A lawful basis has to exist for the AI processing specifically
Article 6(1) reads: “Processing shall be lawful only if and to the extent that at least one of the following applies”, and it applies to each purpose separately. The basis that covered holding employee records therefore does not automatically cover analysing them with a new system. In an employment relationship consent is rarely a sound basis, because of the imbalance between the parties, which usually pushes the analysis to necessity or to legitimate interests and its balancing test.
- Required checks
- Write down the purpose of the AI processing in one sentence, then identify the basis for it.
- Where the basis is legitimate interests, complete and keep the balancing assessment.
- Where data is being re-used, run the Article 6(4) compatibility assessment rather than assuming.
Storage limitation reaches the embeddings
Personal data must be kept in a form which permits identification of data subjects for no longer than is necessary for the purposes for which the personal data are processed. Deleting a source document does not delete its embedding, its cached completion or the prompt that quoted it, and an erasure request under Article 17 reaches all of them.
- Required checks
- Extend the retention schedule to the index, logs, caches and any fine-tuning datasets.
- Test an erasure request end to end before launch, and record the result.
- Vendor questions
- What is your deletion SLA, and does it cover backups?
- Technical controls
- Cascade deletion from source document to embedding to cache.
Standard contractual clauses — the right module, actually completed
The Commission’s clauses provide appropriate safeguards under Article 46(1) and (2)(c) and combine general clauses with a modular approach for different transfer scenarios. The common failure is not the absence of clauses but the wrong module, empty annexes, or a docking clause that nobody completed for the party that later joined.
- Required checks
- Confirm which module applies to the actual relationship, and that it is the one used.
- Check the annexes are filled in with real descriptions rather than "as per the agreement".
- Confirm the transfer impact assessment sits alongside the clauses.
- Vendor questions
- Which module of the 2021 clauses do you sign, and can we see the completed annexes?
Human review required — take this to your counsel
Article 32 — appropriate measures, including for the new stores
Security measures must be appropriate to the risk, taking account of the state of the art and the nature of the processing. For a retrieval system the specific risks are cross-tenant or cross-user retrieval, prompt logs holding content the source system protected, and an index that outlives the permissions it was built from.
- Required checks
- Confirm retrieval enforces the source system’s permissions at query time.
- Confirm the breach process covers the index and the prompt logs, not just the application database.
- Vendor questions
- How is tenant and user isolation enforced in retrieval, and has it been tested by a third party?
- Technical controls
- Encrypt the index and the logs at rest with separate key management from the application.
- Re-synchronise permissions on a schedule so revoked access is revoked in the index too.
The model provider is usually a subprocessor
A processor may not engage another processor without authorisation, and must impose the same obligations down the chain. In an AI product the chain is often longer than the buyer realises: the application vendor, the cloud host, the model provider, and sometimes a separate moderation or evaluation service. Each one is a place your content goes.
- Required checks
- Get the current subprocessor list and read it as an architecture diagram.
- Confirm the notice period before a subprocessor changes, and what you can do about it.
- Vendor questions
- Which model provider do you use, in which region, and is that a contractual commitment or a current fact?
- How are we notified of subprocessor changes, and can we object?
Articles 13 and 14, plus AI Act transparency duties
People have to be told about the processing, and where there is automated decision-making within the meaning of Article 22 they must be given, at least in those cases, meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing for the data subject. The AI Act adds its own transparency duties, which have applied since 2 August 2026.
- Required checks
- Update the privacy notice for the specific AI processing, not with a generic AI paragraph.
- Prepare the explanation of the logic in language a person affected would actually follow.
- Check whether interactions with the system need to be disclosed as AI-generated.
- Vendor questions
- What documentation do you provide about how the model produces its output, at a level we can pass on?
- Technical controls
- Label AI-generated content in the interface, not only in the terms of use.
The vendor is established outside the EU
Where a vendor and its parent are established decides which courts and which disclosure laws reach your content, independently of where the servers are. An EU subsidiary of a non-EU group is a different answer from an EU-headquartered company, and it is worth knowing which one you are buying from before the contract is signed.
- Required checks
- Identify the contracting entity, its country of establishment, and its ultimate parent.
- Check whether an EU representative is appointed where one is required.
- Vendor questions
- Which legal entity contracts with us, and where is it established?
- How do you respond to a request from a non-EU authority for customer content?
Structured issue-spotting to support your own review — not legal advice. Verify against the cited primary sources and your counsel.
03What this reading does not know
- Whether any of the data falls into a special or sensitive category.
- Whether any material is covered by legal professional privilege.
- Whether prompts or documents leave the company network.
- Whether a person reviews the output before it is acted on.
04Instruments these issues point at
- regulationEU SCCsThe Commission’s standard contractual clauses for transferring personal data to third countries under Article 46(1) and (2)(c) GDPR. Built as general clauses plus modules for controller-to-controller, controller-to-processor, processor-to-processor and processor-to-controller transfers; the right module has to be chosen for the actual relationship.
- guidanceEDPB Opinion 28/2024European Data Protection Board opinion, requested under Article 64(2) GDPR, on when an AI model can be considered anonymous, on legitimate interest as a basis for developing and deploying AI models, and on the consequences of unlawful processing during development. It is an opinion, not a binding instrument.
- statuteGDPRThe EU’s general data protection regulation. For AI deployments the operative articles are 5 and 6 (principles and lawful basis), 9 (special categories), 13 and 14 (information), 22 (automated individual decision-making), 28 (processors), 32 (security), 35 (data protection impact assessment) and Chapter V (transfers to third countries).
- statuteEU AI ActThe EU’s horizontal AI regulation, applying by risk class. Annex III point 4 covers employment, worker management and access to self-employment, including systems used to recruit or select people and to evaluate candidates. Chapter III Sections 1 to 3, which carry the high-risk obligations, now apply from 2 December 2027 for Annex III systems.
- regulationDigital Omnibus on AIAmends the AI Act and two product regulations to simplify implementation. Its effect on planning is the timetable: the high-risk rules in Chapter III Sections 1 to 3 move to 2 December 2027 for Annex III systems and 2 August 2028 for Annex I systems, and new prohibitions and an Article 50(2) transition arrive on 2 December 2026.
05Vendor documents being watched
- Privacy policyhttps://www.harvey.ai/en-US/legal/privacy-policynot yet fetched
- Security pagehttps://www.harvey.ai/en-US/securitynot yet fetched
- Security pagehttps://trust.harvey.ai/not yet fetched
- Terms of servicehttps://www.harvey.ai/en-US/legal/terms-of-servicenot yet fetched
06Ask about your own deployment
This page reads the rules against a generic organisation. Your size, industry, data and existing contracts change which of these issues matter and which fall away.