In 2026, an interesting incident occurred at Meta. For almost two hours, large volumes of internal company and user data were accessible to every engineer working there. The normal access controls in place had almost ceased to exist. An AI agent had been used earlier to help with a routine task. It had posted the answers to an internal forum without asking for user permissions. Meta had described the incident as “Sev-1”, its second highest severity level.
This highlights an issue most organizations face. A 2026 Deloitte report found that of more than 3000 business leaders asked, 74% expected their organization to be proficient in using AI agents. However, only 21% of them had a mature governance model in place for these AI agents.
This makes for an alarming statistic when read alongside Gartner’s prediction that by the end of 2026, organizations are likely to abandon 60% of their AI projects that AI-ready data cannot support.
An enterprise AI use case refers to a specific business task where an AI is run in production. It relies on company data to accomplish its tasks at the organizational scale, with the risks dependent on how sensitive the data in question is and how much autonomy it retains.
Some of the most common and critical use cases are discussed below, with further information on what data they touch, what governs it, and how they can be secured. Read on to learn more.
What Makes An AI Use Case “Enterprise”?
For any use case to be considered “enterprise” in nature, it must satisfy three basic parameters. Firstly, it must run in production, always in a live workflow rather than a demo. Secondly, it must operate at an organizational scale, across multiple users, teams, and systems. Thirdly, it must draw on the company’s proprietary data, under strict access controls, with appropriate audit trails and an accountable owner.
While consumer AI serves one person, with the data being provided by that one person only, enterprise AI serves an entire organization with data it can be held accountable for.
Consider a marketer drafting their copy in a public chatbot with a claims workflow that can read customer data. It drafts a decision and escalates an exception. The latter would be an example of an enterprise use case, as this is where the data risk is present and at its highest.
Moreover, this is the precise reason why capability alone rarely decides what can be shipped. Data readiness and the cumulative risk assessments do. An S&P Global report found that 46% of all organizations working on AI projects end up having to scrap these projects between the PoC and broad adoption phase.
The organizations that do end up successfully leveraging such capabilities are the ones that have appropriate compliance, risk, and data availability, and appropriate measures in place when choosing such projects. In short, the model itself is the easy part; it’s the data management practices that most organizations wishing to leverage enterprise AI struggle with.
Enterprise AI Use Cases By Industry
Each of the following use cases covers AI’s most impactful use in that industry, what data it touches, what governs it, and what to secure first.
Financial Services
The most relevant use for enterprise AI systems in the financial sector is in fraud detection and AML monitoring. Leveraging these capabilities, transactions can be monitored with fraud models scoring every payment, account, and customer against known patterns of abuse. At the time of Google Cloud’s launch announcement for its AML AI, it was announced that more than 95% of alerts from rules-based systems are defined as false positives in the first-phase review, with a further 98% never leading to a suspicious activity report.
BSA/AML obligations, GLBA Safeguards Rules, and PCI DSS are some obligations and frameworks such use cases must be compliant with. This requires strict classification and restriction of datasets being fed into the models, especially KYC and PII details, with documentation of lineage to the alert score, enabling appropriate context to the examiner.
Healthcare
Several services have begun offering ambient scribes that listen to a patient’s visit, draft the clinical notes, and queue them for the physician to review and sign. This has helped deliver incredible value, with the Permanente Medical Group’s one-year review in NEJM Catalyst stating that documentation saved more than 15,700 hours, or 1794 working days, compared to non-users. Moreover, per a JAMA Network Open quality-improvement study, clinicians’ burnout fell from almost 52% to only 38.8%.
HIPAA’s Privacy & Security Rules are the most immediate rules that would affect such use cases. Additionally, various countries with their own data privacy regulations may have different obligations for those handling users’ medical data. As a result, organizations leveraging such capabilities must find and classify all PHI, set and enforce retention limits, and have strict access controls in place. It must extend these obligations to any vendors.
Government
Citizen-service assistants have proven incredibly helpful in answering questions about state benefits, permits, licenses, taxes, and other various transactions for residents. However, they can also fail, and fail very publicly, as the NYC’s MyCity chatbot, built on Microsoft’s Azure, was meant to help SMEs with city rules. Started in 2024, it gave inaccurate and potentially illegal advice, leading to its closure in February 2026.
Almost every regulation that deals with federal data in the US, such as FISMA, FedRAMP, and OMB M-25-21, requires organizations to adopt minimum practices such as pre-deployment tests, impact assessments, and ongoing monitoring for high-impact AI that affects accessibility to government services. In the EU, these extend to requirements to inform the public that they’re interacting with an AI. Hence, entitlement-based retrieval must be enforced from the very start so the AI assistants can only surface records the person asking is authorized to see. Additionally, all PII must be classified in its knowledge base, with detailed query logs for future audits and inquiries.
Manufacturing
Large factories and other development facilities can use predictive maintenance models to read vibrations, temperature, and other sensors to flag any failing equipment before it becomes an operational roadblock. Per Siemens, BlueScope, the Australian steelmaker saved almost 2,000 hours of unplanned downtime over a period of three years while also avoiding 53 complete process interruptions after they began using Siemens’ Senseye.
Industry standards such as IEC 62443 and the NIST Cybersecurity Framework govern how industrial security, in addition to customer and supplier contracts, must be managed. Hence, every flow of data from the plant floor into the cloud AI must be mapped and classified before it leaves the site, with the AI pipelines strictly kept separate from control systems.
Why Enterprise AI Use Cases Stall On Data
Model underperformance is rarely, if ever, cited as the reason why use cases are stalled. Projects end up stalling for four primary reasons.
- Nobody knows where the data is: Thales’ 2026 Data Threat Report found only 34% of organizations have complete visibility into their data infrastructure. Moreover, out of those that do have it, only 39% can appropriately classify it. No AI use case can be effectively scoped, approved, or audited against when the required data hasn’t been properly audited.
- Assistants inherit overprivileged unreviewed access: Microsoft’s own documentation states Copilot currently operates within existing permissions, and that overshared or poorly governed content can lead to increased risk of loose permissions turning into massive exposure.
- Sensitive data gets used in the pipeline: Whatever is loaded in the retrieval index or training set will carry over its sensitivity. Hence, model inversions that lead to sensitive data being extracted are the costliest sort of AI-related breach per an IBM study.
- AI runs without oversight: Shadow AI incidents have nearly doubled to 43% in 2025 from 20% in 2024. Almost 68% of companies that fell victim to such incidents did not have AI governance policies in place to manage or detect AI.
Every use case illustrated earlier runs into at least one of these issues.
How Securiti Helps
It’s one thing to know a problem; it’s another to address it.
Each of the aforementioned four problems sits at the very top of the data layer. And this is precisely where data security posture management (DSPM) can be of help.
Securiti, now a Veeam company, has the DSPM solution that continuously finds where the sensitive data resides, who and what has access to it, and how exposed it is. Moreover, it extends this view to AI.
Leveraging this solution, organizations can automatically discover cloud-native, shadow, and dark data across clouds, classify sensitive and regulated data using AI and context, and label files by policy. Additionally, it can map which users, roles, and AI systems are entitled to which files, tables, and sensitive data, enforce policy-based least-privilege access, and mask sensitive data dynamically. At the same time, it also catalogs the sanctioned and shadow AI models, assessing their security posture and their exposure to the OWASP Top 10 for LLMs, while applying context-aware LLM firewalls to prompts, retrieval, and responses.
Request a demo today and learn more about how DSPM can aid your organization’s specific enterprise AI use cases.
Frequently Asked Questions
Some of the most commonly asked questions related to enterprise AI use cases are as follows:
