Executive Summary:

The EU AI Act is now enforceable, carrying penalties up to 7% of global turnover. Many executives mistakenly assume that deploying Salesforce’s native AI (Agentforce, Einstein) guarantees compliance. However, under the Act’s shared responsibility model, Salesforce only secures the AI system itself. Your organization is strictly responsible for the underlying data.

Salesforce cannot verify if your data is lawfully sourced, properly masked, or free of bias. If an autonomous agent acts on restricted or poisoned data, the legal liability falls on you. Furthermore, customizing or branding these AI tools can legally reclassify your company from a "deployer" to a "provider," significantly increasing your regulatory burden.

The December 2027 compliance deadline for high-risk AI is a narrow window to build a governed data foundation. To protect your Salesforce AI investments, leadership must immediately prioritize:

• Data Masking: Keep production personal data out of AI development environments.
• Automated Erasure: Ensure consumer deletions propagate through all backups and archives.
• Archiving & Encryption: Limit the AI data footprint and lock down sensitive fields.
• Resilience: Enable granular backup and restore to instantly reverse AI-generated errors.

Odaseva provides the essential data security tools to ensure your AI deployments remain both powerful and legally compliant.

Introduction: Why Deploying Agentforce Doesn't Make You Compliant with the EU AI Act

Since August 2, 2026, the EU AI Act has teeth. Market surveillance authorities across the 27 Member States are operational, transparency obligations apply, and the penalty regime is live.

Yet many of the enterprises we speak with in EMEA are working from a comfortable but inaccurate assumption: that because Salesforce has invested heavily in Trusted AI, deploying Agentforce or Einstein on Salesforce infrastructure means the compliance question is resolved. Such customers of Salesforce believe they aren’t responsible for ensuring their Salesforce data complies, because Salesforce does the work for them. 

This isn’t true. And the reason is structural, not technical.

The EU AI Act splits responsibility between the organization that builds an AI system and the organization that uses it. Salesforce is the provider. Salesforce customers are the deployer. Salesforce's compliance work covers Salesforce's system — the model, the guardrails, the technical documentation. It does not cover the most critical aspect, which actually determines whether your AI is safe, lawful, and defensible: your data.

This is the same shared responsibility gap that caught organizations out under DORA, and before that under GDPR. If you have read our analysis of DORA's impact on Salesforce data, this will sound familiar. The AI Act simply raises the stakes, because an AI agent doesn't just read your data — it acts on it.

Let's break down exactly where the line of responsibility sits, and what falls on your side of it.

What the EU AI Act requires, and when

The timeline shifted significantly in 2026. The AI Omnibus — politically agreed on May 7, 2026 and in force since July 27, 2026 — deferred the high-risk obligations by roughly sixteen months.

Here is where the regulation actually stands today:

Read that December 2027 date as an opportunity, not a reprieve.

Sixteen months is roughly one enterprise budget cycle. It’s enough time to build a governed data foundation before your high-risk obligations bite. It is not enough time to retrofit governance onto a Data Cloud estate that has already ingested five years of unclassified CRM history, wired it into a dozen agents, and shared it with three downstream analytics platforms.

The organizations that treat 2027 as a deadline will be remediating. The organizations that treat it as a build-window will be compliant.

The penalties for non-compliance with the EU AI Act

The AI Act sets three tiers under Article 99:

• Up to €35 million or 7% of global annual turnover — for breaching the Article 5 prohibitions. These are already enforceable.
• Up to €15 million or 3% of global annual turnover — for breaching most other obligations, including the data governance, record-keeping, robustness and deployer duties described in this article.
• Up to €7.5 million or 1% — for supplying incorrect or misleading information to authorities.

And the AI Act does not displace GDPR. A single failure — training an agent on personal data you had no lawful basis to use — can be penalized under both regimes simultaneously, with GDPR adding up to €20 million or 4% on top. With stakes this high, non-compliance is a cost enterprises can’t afford to risk.

Provider vs. deployer: the shared responsibility model for AI

The AI Act defines two primary roles:

• A provider develops an AI system and places it on the market under its own name. 
• A deployer, under Article 3(4), is any natural or legal person using an AI system under its authority in a professional capacity.

When you switch on Agentforce, Einstein, or a Data Cloud–grounded predictive model, Salesforce is the provider. You are the deployer. And the deployer’s obligations in Article 26 are yours alone — they cannot be outsourced to your platform vendor, because they attach to decisions only you can make.
Here is how the line actually falls:

Salesforce can build you a safe engine. It cannot tell you whether the fuel you poured into it was lawful.

The trap: when a deployer becomes a provider

Many enterprises will not remain deployers, and most of them don't know it yet.

Article 25(1) reclassifies a deployer as a provider — inheriting the full weight of Articles 9 through 17 — in three situations:

1. You put your own name or trademark on the high-risk AI system.
2. You make a substantial modification to it.
3. You modify the intended purpose such that a system that was not high-risk becomes high-risk.

Now picture the standard enterprise Agentforce project. You build custom topics and actions. You ground the agent in your own Data Cloud. You brand it with your company's name and expose it to your own customers.

You have arguably done all three.

At that point Article 10 — the full data governance regime covering data origin, preparation, representativeness and bias examination — applies to your dataset, not Salesforce's. This is why the data governance question cannot wait for a legal opinion on classification. The data work is identical either way; only the paperwork changes.

Is your Salesforce AI actually high-risk?

Not every agent is. A service deflection chatbot generally isn't. But a typical enterprise Salesforce estate hides a handful of Annex III use cases inside a large benign one:

• Employment and worker management — recruiting, task allocation, promotion and performance evaluation. If your recruiters work in Salesforce, this is you.
• Access to essential private services — creditworthiness evaluation and risk pricing for life and health insurance. This is Financial Services Cloud, almost by definition.
• Education and vocational training — admissions, assessment, proctoring.
• Critical infrastructure, biometrics, law enforcement and migration — for the relevant sectors.

Which produces an uncomfortable first question: can you currently list which of your AI systems touch which of these categories?

You cannot answer that without knowing which data feeds which agent. Classification is a data lineage problem before it is a legal one.

And two things that are already illegal

The Article 5 prohibitions have been enforceable since February 2025, carry the 7% penalty tier, and include two practices that appear in CRM deployments with alarming regularity:

• Emotion inference in the workplace (Article 5(1)(f)). Not sentiment analysis on your customers — sentiment analysis applied to your own employees, such as scoring service agents on the emotional tone of their interactions. This is a prohibited practice, not a high-risk one, and no December 2027 grace period applies.
• Social scoring (Article 5(1)(c)) — evaluating people based on social behavior or personal characteristics in ways that lead to detrimental treatment in unrelated contexts.

What "compliant" with the EU AI Act (and GDPR) actually looks like

The chart below details:

• The obligation under the EU AI Act and GDPR
• Who is responsible for complying with it (note how many of the rows marked deployer have no plausible owner other than you!)
• As of which date its enforceable
• What you need to do in order to comply with the obligation
• Odaseva’s product that solves for the obligation

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Now we’ll take a deep dive into the seven rules you’re responsible for under the EU AI Act (and GDPR).

You’re responsible for these 7 rules under the EU AI Act (and GDPR): 

Here is each obligation you’re responsible for, the failure mode it creates in a Salesforce environment, and how to resolve it.

Rule 1: Keep production personal data out of AI development environments

Article 10(5) permits processing special categories of personal data for bias detection and correction only under strict safeguards — including state-of-the-art security and privacy-preserving measures, including pseudonymisation, strict access controls, a prohibition on transmission to other parties, and deletion once the bias correction is complete.

Separately, GDPR Article 5(1)(c) and Article 25 have always made it difficult to justify a full-copy sandbox containing live customer PII.

The risk: Your AI teams need realistic data to tune and evaluate an agent, so they request a full sandbox refresh. That sandbox now holds all your customer records, with weaker access controls than production environments, a longer lifespan, no purpose limitation, and a developer population that never appeared in your ROPA. It’s the same critically valuable and regulated data, but with higher risk of breach.

This is the single most common finding in Salesforce privacy audits — and the AI Act converts it from a data protection issue into a data governance failure that undermines your entire model development lifecycle.

How to close it: Odaseva Data Masking and Sandbox Seeding lets AI teams work with realistic, structurally faithful datasets that contain no real personal data. If the sandbox environment is compromised by internal or external threat actors, your real and sensitive data isn’t exposed.

There are multiple options for achieving this, but these three capabilities make the difference between anonymization that works for AI and anonymization that breaks it:

• Format-preserving masking. Emails, phone numbers, IBANs, credit card numbers, national identifiers and IP addresses are masked according to their semantics, not blanked. Your validation rules, integrations and agent logic still exercise realistic input.
• Statistical generalization. Ages are banded into cohorts and dates are shifted within controlled windows, rather than replaced with constants. The individual disappears; the distribution survives. That distinction is what makes a masked dataset usable for evaluating a model at all.
• Referential integrity across Orgs. Anyone can null out a field. Preserving relationships across a complex Salesforce object graph — so Accounts still match Contacts, Opportunities and Cases after transformation — is the hard part, and it's what makes seeded environments behave like the real thing.

The compliance outcome is straightforward: your AI teams get the data fidelity they need, and your sandboxes stop being a shadow copy of your customer base. 

Why Odaseva: 

With Odaseva Data Masking, you can:

• Mask your data at scale: Scalable data masking and anonymization in sandboxes prevents unauthorized access from internal or external developers.
• Fast-track your masking jobs: Prepare sandboxes for testing and development 10x faster than other solutions, saving valuable time and resources.
• Get experts on your side: Let our team of experts provide you guidance for initial data masking setup and job optimization.

With Odaseva Sandbox Seeding, you can: 

• Protect sandbox data at scale: Scalable data masking and anonymization in sandboxes prevents unauthorized access to regulated data.
• Take full control of your data: Seed sandboxes with unlimited parent-child relationships and gain visual targeting across all relationship levels.
• Get experts on your side: Let our team of experts provide you guidance for initial sandbox seeding setup and data seeding job optimization.

Learn more about Odaseva Data Masking and Odaseva Data Seeding.

Rule 2: Make erasure reach every copy — including the ones feeding your AI

GDPR Article 17 gives individuals the right to erasure. The AI Act does not weaken it. What AI architecture does is multiply the places a record can hide.
The risk: A customer exercises their right to be forgotten, so your team deletes the record in Salesforce and closes the ticket.

The record still exists in your last twelve backups. In your archive tier. In the Data Cloud data model object built from a nightly share. In the Snowflake table your analytics team created. And potentially, embedded in a vector index, in a form that can be surfaced by an agent that never queries Salesforce at all.

Your DPO has just signed an erasure confirmation that is factually incorrect. Under GDPR, that's a €20 million exposure. Under the AI Act, it also means your training and grounding data contains personal data you have no lawful basis to hold.

How to close it: Odaseva Data Privacy automates consumer rights across the full data estate, not just the production Org, via:

• In-Org request management. Right to be Forgotten and Right of Access panels install directly on Contact, Lead and Person Account, with dedicated permission sets separating administrators from business users. Every request is tracked with its requested date, actual processing date, status and error details — giving you an auditable record of the one-month statutory deadline, inside your own Org.
• Erasure that reaches backups and archives. Deleting from production is the easy part. Odaseva propagates erasure into backup datasets and archived stores, so your immutable copies don't quietly re-create the liability you just resolved.
• Certified propagation. This is the capability most worth understanding in detail. Odaseva can verify that an anonymization or deletion correctly propagated to related and child records, and produce a report proving it.

That last point is the difference between telling an auditor you deleted the data and showing them evidence that you did — across a relational Salesforce graph where a single Contact deletion touches Cases, Opportunities, Activities, Files and custom objects.

If you are building AI on Salesforce data, ask your team one question: when we erase a customer, can we prove what happened to every downstream copy? If the answer involves a spreadsheet, you have a gap and therefore a non-compliance risk.

Why Odaseva:

With Odaseva Data Privacy, you can: 

• Enforce compliance at scale: Parallel processing accelerates workloads and keeps pace with expected performance.
• Easily accommodate your complex data models: We support complex data models spanning 40+ levels of relationships and custom logical relationships in your data hierarchy, guaranteeing that no record or field is left behind.
• Partner with data privacy experts: Collaborate with our team to define your compliance and data lifecycle strategy, then allow us to configure the perfect solution to perfectly meet your specific  requirements.

Learn more about Odaseva Data Privacy.

Rule 3: Shrink your AI data footprint through archiving

Article 26(4) is the deployer obligation most likely to catch enterprises out. It requires that, where you exercise control over input data, you ensure that input data is relevant and sufficiently representative in view of the intended purpose of the high-risk AI system.

In a Salesforce estate, you control the input data absolutely. There is no ambiguity about whose duty this is.
The risk: Salesforce Data Cloud ingests everything, because ingesting everything is easier than deciding what matters. Duplicate accounts from an unfinished merge. Contacts who left their company in 2011. Test records from a migration that failed in 2019. Closed-lost opportunities that systematically skew a propensity model.

Your agent grounds on all of it. And when a regulator asks you to demonstrate that your input data was relevant, you have no answer — because nobody ever defined what relevance excluded.
How to close it: Odaseva Data Archiving turns retention policy into an enforced, evidenced boundary rather than a document nobody reads.

Aged and obsolete records move out of the operational Org into a governed archive with its own access controls and its own audit trail. Three things follow:

1. Your AI grounding set becomes defensible. The data your agents see is the data you decided was relevant — and you can show the policy that made that decision.
2. GDPR storage limitation stops being aspirational. Article 5(1)(e) requires that personal data not be kept longer than necessary. Archiving with defined retention is how that becomes real.
3. Archived data stays accessible without re-entering the AI surface. Users — and, where appropriate, agents — can search and view archived records through a controlled interface that respects the requesting user's sharing rules, rather than re-importing everything into the Org "just in case."

There is a performance argument here too, and it's not trivial: a smaller, cleaner grounding set produces better retrieval and fewer hallucinated answers than a large, noisy one. Compliance and AI quality point in the same direction.

Why Odaseva: 

With Odaseva Data Archiving, you can:

• Unlock deep data: Unlock your deepest data with advanced JSON queries—access 30+ levels of nested information that standard queries can’t reach.
• Simplify archiving with user-friendly UX: Eliminate archiving risks in complex data hierarchies with visual data management using Odaseva Archiving.
• Fast-track file extraction from backups: Accelerate file extraction by up to 100x using existing backups to unlock the value of your historical data faster and easier.

Learn more about Odaseva Data Archiving.

Rule 4: Encrypt and localize sensitive fields so your AI never sees them

Two obligations converge here. GDPR Chapter V governs transfers of personal data outside the EU. AI Act Article 10(5) requires strict access controls and a prohibition on onward transmission for the most sensitive categories. And Article 26(6) requires deployers to keep the logs automatically generated by that high-risk AI system… for a period appropriate to the intended purpose… of at least six months.
The risk: Your Org is configured for EU data residency, so residency is considered solved. Then a field containing health information is included in a prompt, and travels to an inference endpoint whose processing region nobody documented. Meanwhile, six months later, nobody can reconstruct which fields an agent read, on whose authority, or for what purpose.

How to close it: Odaseva Data Encryption, built on our Zero Trust Vault architecture, takes a fundamentally different approach from Org-level residency controls: it enforces protection field-by-field.

Sensitive values are encrypted and held in a vault under a customer-controlled key, in a customer-declared region. What Salesforce stores is a mask plus a reference — not the value.

The consequence is worth stating plainly. Einstein, Data Cloud, Agentforce, and any third-party tool with a connection to your Org see ******** by default. The sensitive value was never present on the AI-reachable surface in the first place. Residency stops being a promise about a data center and becomes a property of the field itself, enforced per data subject where you need it — so a German customer's data can be governed differently from a UK customer's, within the same object.

Two further properties matter for AI Act evidence:

• Protected data stays searchable. Encryption that breaks search gets switched off. Selective searchability is what keeps the control in place under business pressure.
• Every access is logged with its stated purpose. Each request to a protected field records the user, timestamp, source IP, correlation ID, the declared purpose of the access, and the exact version of the security policy in force at that moment.

That last capability is not a log file. It is an evidentiary record that reconstructs both what happened and the rule that permitted it — which is precisely what Article 26(6) is asking you to retain, and what GDPR's purpose limitation principle has always required you to be able to demonstrate.

Why Odaseva:

With Odaseva Data Encryption, you can:

• Simplify vault management: Our centralized console unified UI simplifies vault management (policies, users, security), reducing errors, improving efficiency, and providing centralized visibility.
• Seamlessly access vault data: Interact with your most confidential data directly in Salesforce, improving efficiency and compliance.
• Monitor everyone, including admins: Get real-time SIEM monitoring (Datadog, Splunk, etc.) of both user and admin activity in the vault, ensuring no privileged action goes unmonitored.
• Get experts on your side: Let our team of experts provide you guidance for the initial setup of your vault.

Learn more about Odaseva Data Encryption.

Rule 5: Feed your AI accurate and relevant data — the foundation of bias mitigation

Article 10(3) requires that training, validation and testing datasets be relevant, sufficiently representative, and to the best extent possible, free of errors and complete in view of the intended purpose. Article 10(2) adds an obligation to examine datasets for possible biases likely to affect fundamental rights or lead to discrimination.

Let's be precise about what technology can and cannot do here, because the market is full of overclaiming.

No data platform can tell you whether your model is biased. That requires domain judgment about which attributes are protected, which outcomes matter, and what fairness means in your specific context. It is a governance exercise supported by tooling, not a product you buy.

But bias analysis on bad data is worthless. If your dataset is riddled with duplicates, stale records, systematic gaps and invalid values, any fairness measurement you run on it measures your data quality problem, not your model. Article 10's error-freeness and completeness requirements exist precisely because they are the precondition for everything else in that article.

The risk: A bias assessment concludes that your model under-serves a particular customer segment. Six weeks of investigation later, the real finding is that the segment was systematically under-represented in the CRM because a 2018 integration silently dropped records — and nobody knew.

How to close it: Odaseva Backup and Restore and Odaseva Data Privacy give you the data quality foundation Article 10 assumes you already have:

• Dataset comparison and change reporting between any two points in time, so you can identify when and where records were lost, corrupted or systematically altered.
• Org-level data and configuration audits covering empty and invalid fields, permission and profile configuration, record type visibility, and automation bypasses — the structural issues that silently distort what data actually reaches your AI.
• Lifecycle management that removes obsolete records from the grounding set, so relevance is enforced rather than assumed.

Get this foundation right and your fairness assessment measures your model. Skip it and you will spend your Article 10 budget rediscovering data quality problems.

Learn more about Odaseva Backup and Restore and Odaseva Data Privacy.

Rule 6: Reverse what an autonomous agent gets wrong

Article 15 requires high-risk AI systems to be resilient against attempts to alter their use, outputs or performance by exploiting vulnerabilities — and names data poisoning and model poisoning explicitly. Article 26(5) requires deployers who identify a risk to inform the provider or distributor and the relevant market surveillance authority, and suspend the use of that system.

This is where the AI Act stops being a documentation exercise.
The risk: Previous generations of Salesforce AI made predictions. Agentforce takes actions — it writes to your core objects.

An agent with write access executes a flawed action across 200,000 records. The cause might be a defect in topic logic, a knowledge article that was quietly poisoned, or a prompt injection carried in an inbound customer email that the agent dutifully treated as an instruction. Every downstream flow, trigger and integration fires on the corrupted data. The blast radius is your entire Salesforce estate, propagating at machine speed.
Two questions decide the outcome, and both are data questions: How quickly can you identify exactly what changed? And can you reverse it without reversing everything else that happened that day?
How to close it: Odaseva Data Backup & Restore is the control that makes agentic AI adoption survivable:

• High Backup frequency: an autonomous agent can do a great deal of damage inside a nightly backup window.
• Comparison-based forensics. Differentiate two points in time to produce an exact report of what changed. That report is how you scope the incident — and it is the input to any notification you owe under Article 26(5).
• Granular, surgical restore. Restore a single record and its child records, rather than rolling back the entire Org. This matters more than it sounds: an Org-wide rollback destroys every legitimate change made in the same window, which is why executives refuse to authorize one. A restore nobody will approve cannot be considered a control.
• Automation control during recovery. Odaseva can deactivate triggers, flows and validation rules during a mass restore, then reinstate them. Without this, restoring 200,000 records re-fires the very automations — and potentially the very agent — that caused the incident.
• Immutable backups with customer-managed keys. A backup that a compromised process cannot alter is the literal technical answer to Article 15's data poisoning requirement.

If your board has approved autonomous agents with write access to customer data, this is the control that lets you say yes.

Why Odaseva:

With Odaseva Backup and Restore, you can:

• Test your recoverability: Run audits on data and metadata to get a view of Salesforce Org configuration, and assess your restore readiness to ensure a reliable recovery.
• Transform your data and metadata: Enable flexible and customized data use post-restore by pre-transforming data and metadata to meet your organization’s needs and avoid potential restoration blockers.
• Quickly correct your own mistakes: Allow Salesforce end-users to self-manage field changes, freeing up admin and operation resources.
• Get experts on your side: Let our team of experts provide you custom solutions and assurance that you can recover your Salesforce data.

Learn more about Odaseva Backup and Restore.

Rule 7: Enforce a permission model your agents cannot exceed

Article 14 requires that high-risk AI systems be subject to effective human oversight. Article 26(2) makes it concrete for deployers: you must assign human oversight to natural persons who have the necessary competence, training and authority, as well as the necessary support.

Authority is the word to focus on. Human oversight is meaningless if the agent operates under a permission model the overseer cannot constrain.

The risk: The agent runs as an integration user with a broad profile, because that was the fastest way to get the pilot working and nobody revisited it before go-live. Every field that the user can see, the model can retrieve — and potentially surface to whoever is talking to the agent.

Worse, prompt-level guardrails are the only thing standing between a determined user and that data. Prompt instructions are advisory. Access control is not.

How to close it: There are two layers to consider:

• First, audit what your agents can actually see. Odaseva's Org analysis capabilities surface profile and permission set configuration, record type visibility and automation bypasses — answering the question "what can this principal actually access?" before an agent goes live, not after an incident. Treat every agent's running user as a privileged account subject to a least-privilege review, because that is exactly what it is.
• Second, enforce the permission model below the prompt. Odaseva's patent-pending work in this area treats an AI agent as a security principal in its own right, with the source application's authorization model — object permissions, field-level security, sharing rules — compiled into database-native row and field policies that apply uniformly to human users, automation and AI agents alike, with per-principal audit of every access.

The design principle is simple and it is the direction the whole market is moving: an agent should be structurally incapable of retrieving data its principal is not entitled to see — not merely instructed not to. When your DPO asks how you can be sure an agent never exposed a restricted field, "we told it not to" is not an answer. "The database refused the query, and here is the log" is.

Frequently asked questions

Does using Salesforce make my organization AI Act compliant? No. Salesforce is the provider of Agentforce and Einstein and is responsible for the system it builds. Under Article 3(4) you are the deployer, and the Article 26 deployer obligations — input data relevance, human oversight, log retention, monitoring and suspension — apply to you directly and cannot be transferred to your platform vendor.

Is Agentforce a high-risk AI system? It depends entirely on your use case, not on the technology. An agent supporting recruitment, performance evaluation, creditworthiness assessment, insurance risk pricing or access to essential services falls under Annex III and is high-risk. A customer service deflection agent generally does not. Most enterprises have both, which is why classification requires knowing which data feeds which agent.

When do the EU AI Act high-risk rules actually apply? Following the AI Omnibus, December 2, 2027 for standalone Annex III systems and August 2, 2028 for AI embedded in regulated products. The Article 5 prohibitions and AI literacy obligations have applied since February 2025, and transparency obligations since August 2, 2026.

Can I become a provider without realizing it? Yes. Under Article 25(1), branding a high-risk system as your own, substantially modifying it, or changing its intended purpose so that it becomes high-risk makes you a provider — with the full Article 9–17 obligations, including Article 10 data governance over your own datasets. Customizing and branding an Agentforce agent for your customers can trigger this.

Does the AI Act replace GDPR? No. They apply in parallel and can be enforced simultaneously. The AI Act explicitly leaves data protection law untouched, so a single failure involving personal data can attract penalties under both regimes.

What data governance should we start with? Start with what you can provide evidence for: Can you state which records feed which agent? Can you prove an erasure reached every copy? Can you show what an agent accessed six months ago? Can you reverse a bad agent action without an Org-wide rollback? Each "no" is a control gap that takes months, not weeks, to close.

Take the next step in governing your Salesforce AI data

The EU AI Act does not ask whether you trust your AI. It asks whether you can prove what your data was, where it lived, who touched it, and what you did when something went wrong.

For 14+ years, Odaseva has protected the Salesforce data of the world's largest enterprises — including some of the most heavily regulated organizations in EMEA. The capabilities the AI Act now demands are the capabilities we have been building all along: masking and sandbox seeding, archiving and lifecycle management, field-level encryption and residency, consumer rights automation, and enterprise-grade backup and restore.

The difference is that they are no longer just data protection. They are the foundation of defensible enterprise AI.

Schedule a personalized discussion here with our experts to map your Salesforce AI use cases against your EU AI Act obligations, and identify exactly where your data governance gaps sit — while you still have a build window rather than a deadline.

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