Financial institutions are wiring machine learning models, analytics platforms, and AI agents into their most sensitive data. Every one of those connections runs on credentials, and the most powerful credentials are the privileged ones: administrator logins, database accounts, service identities that move data between systems without a human in the loop.
That matters more than it did two years ago. IBM’s 2025 Cost of a Data Breach report found that 97% of organizations that suffered an AI-related security incident lacked proper AI access controls. The models weren’t the weak point. The permissions around them were.
For data and AI leaders, this changes who owns the problem. Privileged access management (PAM) used to sit with the security team, far from the data platform roadmap. It now decides whether a bank can trust the output of its own pipelines, and whether it can explain to a regulator who, or what, touched a dataset and why.
Why Privileged Accounts are the Shortest Path to Financial Data
A privileged account is any identity that can change systems, read broad swaths of data or create other accounts. In a bank, this includes database administrators, cloud operators, the service accounts behind ETL jobs, and the connectors feeding feature stores and model training runs.
Regulation already treats these accounts as a control point. PCI DSS, SOX, GLBA, the EU’s DORA and the SWIFT Customer Security Programme all expect institutions to restrict, log and review elevated access. The specifics differ, but the direction is the same: know who holds privilege, limit it, and keep evidence.
Attackers understand this too. Verizon’s 2026 Data Breach Investigations Report found credential abuse present at some stage in 39% of breaches, making it the most pervasive technique in the dataset. It isn’t only how intruders get in. It’s how they move sideways, escalate and reach data once inside. 
The trend isn’t new. In its 2025 edition, Verizon reported that compromised credentials were the top initial access vector at 22% of breaches, and SpyCloud’s analysis of that report noted that 54% of ransomware victims had their credentials exposed in infostealer logs beforehand. Stolen access is cheap. Standing privilege turns it into a disaster.
Where AI and Data Pipelines Expand the Privileged Attack Surface
Every new pipeline adds identities. A retrieval-augmented generation (RAG) connector needs read access to document stores. A training job needs a service account with broad table permissions. An analytics notebook gets a shared credential because someone was in a hurry.
None of these are exotic. They’re the ordinary sprawl of a data platform, and they often escape the review that human administrators get. Shadow AI makes it worse. IBM’s 2025 breach research found that organizations with high levels of shadow AI paid $670,000 more per breach than those with little or none, and that 63% of organizations either lacked an AI governance policy or were still developing one.
The practical lesson is that identity and permissions are becoming shared infrastructure for data, security and AI teams. Hyperight’s piece on the AI-native organization makes a similar point, and reports that NIST launched an AI Agent Standards Initiative in February 2026 to push for interoperable, secure agentic systems. If standards bodies are treating agent identity as a first-class problem, internal governance can’t treat it as an afterthought.
Using Machine Learning to Watch Privileged Sessions
Here the AI community has something to offer, not just something to worry about. Privileged sessions generate rich data: commands typed, systems touched, hours worked, volumes queried. That’s a natural fit for behavioral analytics.
The basic approach is to baseline what normal looks like for each account or role, then score deviations. A database administrator who suddenly exports a customer table at 3 a.m. from a new location stands out. So does a service account that starts querying tables it has never touched.
When machine identity governance in finance is discussed, what is mostly recommended today is a phased path: start with anomaly detection on low-risk, non-sensitive systems, then expand to automated credential rotation and adaptive privilege once the team trusts the signals. That’s sound advice, and it reflects a real tradeoff. Models drift as roles and workloads change. Alert volumes can bury analysts if thresholds are careless. And regulators will ask why a session was flagged or an account was locked, so black-box scores won’t survive an audit on their own.
Treat the model as a triage tool. A human still reviews the session, and the decision is logged.
Agentic AI Needs Its Own Identity Rules
AI agents complicate the picture because they act. They call tools, query databases and trigger workflows, and they do it with credentials. Treating an agent as a shared service account with broad rights is the fastest way to lose track of what happened.
The better pattern is to give each agent its own non-human identity, scope its access to the task, and make that access short-lived. Hyperight’s coverage of metadata-driven guardrails for agents describes enforcing policy at the data layer, where the system can inspect the identity and task of the requesting agent rather than trusting the application above it.
There’s also a governance question underneath the technical one. Kevin Shin’s article on trust as a competitive advantage asks who or what is acting on your behalf, and what approval and logging exist when the actor is software. In a bank, those aren’t philosophical questions. They map directly to audit findings.
These agent-identity decisions tend to surface during tool selection rather than before it, which is one reason finance teams spend most of their PAM evaluation time on the agent and service-account side of the problem rather than on human administrators. Vendor comparison resources, like OneIdentity’s best PAM solution for finance, guide treat agent and non-human identity as a first-class evaluation dimension, alongside the traditional categories of session recording and credential rotation.
A Practical Checklist for Data and Security Leaders
A vendor-neutral starting point:
- Inventory every privileged and non-human account, including service accounts, pipeline credentials and agent identities. You can’t govern what you haven’t counted.
- Remove standing privilege. Grant elevated access just in time, for a defined task and a defined window.
- Record and review privileged sessions, especially those touching customer or trading data.
- Feed session telemetry into your analytics stack so anomaly models have data to learn from and analysts have context.
- Assign a data owner to each access policy. Security can enforce a rule, but someone accountable for the data should approve it.
- Test agent permissions before production. Ask what an agent could reach if its prompt were hijacked, then shrink that.
- Start small. One high-value system with clean logging beats a sprawling program nobody maintains.
Looking Ahead
Expect scrutiny to move from “who has admin rights” to “what can your AI systems reach, and can you prove it.” Agent identity standards are still forming, and regulators operating under frameworks like DORA are already focused on operational resilience and third-party dependencies, both of which now include AI components.
Banks that treat privileged access as part of their data and AI architecture, rather than a separate security checkbox, will be able to answer those questions quickly. Those that don’t will find out during an incident, when the answer costs the most.
*Sponsored Guest Article – This article is paid promotional content provided by One Identity and published by Hyperight as an advertising service. The advertiser is responsible for the claims, opinions, and materials presented. Publication does not constitute endorsement or independent verification by Hyperight.
