Secrets management for financial machine identities and AI agents

At a glance

What this is: NHI Mgmt Group's analysis, drawing on Akeyless's blog post: Financial services are becoming secrets-heavy environments where machine identities, APIs, and AI agents depend on credentials that are often static, scattered, or overexposed.

Why it matters: It matters because identity teams must govern machine and autonomous access with the same discipline they apply to human access, or secrets sprawl will outpace auditability and recovery.

By the numbers:

• Machine identities now outnumber humans 82 to 1 in financial organizations.
• The average breach in financial services now costs $5.56 million, according to IBM’s Cost of a Data Breach Report 2025 cited by Akeyless.

👉 Read Akeyless's analysis of secrets management for financial machine identities and AI agents

Context

Financial machine identities are service and application identities that use secrets to authenticate to systems, data, and pipelines. In finance, they now sit behind payments, trades, customer workflows, and AI-assisted operations, so the control problem is no longer limited to human login hygiene.

The security gap is governance at machine speed. When secrets are stored in code, shared informally, or left unrotated for months, the organisation loses visibility into who or what can authenticate, how long that access lasts, and whether it can be revoked before compromise spreads.

Key questions

Q: What breaks when machine secrets are stored in code or left unrotated?

A: The organisation loses the ability to prove where access exists, who owns it, and whether compromise has already spread. In finance, that creates a reusable credential path into APIs, pipelines, and cloud services. The practical failure is not just exposure, but the absence of a reliable revocation boundary once the secret is copied or leaked.

Q: Why do machine identities increase financial control risk?

A: Machine identities increase financial control risk because they can operate with standing privilege, scale across systems and keep acting after the original business need has changed. In finance workflows, that can allow silent supplier changes, record updates or payment actions without the friction that normally slows human abuse. The risk is hidden privilege plus high velocity.

Q: How can organisations tell whether secrets management is actually working?

A: Look for reduced secret sprawl, faster revocation, and fewer unmanaged copies outside the central system. A healthy programme can show where each secret lives, who owns it, and how quickly it is retired after use changes. If those answers are unclear, the control is cosmetic rather than operational.

Q: Who should be accountable for secrets exposed in AI workspaces?

A: Accountability should sit jointly with IAM, security operations, and the business owners of the workspace. IAM governs privileged access, security teams handle detection and revocation, and business leaders set acceptable-use rules. If the platform stores or reveals secrets, the governance gap is organisational, not just technical.

Technical breakdown

Why static secrets fail in machine-speed environments

Static secrets create a long exposure window because the credential and the workload often outlive the original approval context. In finance, that is especially dangerous when applications, bots, and microservices each hold separate tokens, API keys, or certificates. Once those secrets are embedded in code, config files, or copied across teams, the organisation cannot reliably track where access exists or whether it is still justified. The result is a control plane that looks centralised on paper but remains fragmented in practice.

Practical implication: treat every long-lived machine credential as an exposure window until rotation, inventory, and revocation are demonstrably enforced.

How just-in-time secrets change privilege design

Just-in-time secrets issue access only when a workload needs it and expire it quickly after use. That shifts the control point from broad standing access to task-scoped authorisation tied to context, role, and timing. For machine identities and AI agents, this is not merely an efficiency feature. It changes the trust model by assuming credentials should be ephemeral and reconstructable only at issuance time, not persistently stored for convenience. In regulated environments, that makes review and audit easier because the access event is deliberate rather than permanent.

Practical implication: redesign machine access so the default is issuance on demand, not reusable credentials with standing privilege.

Why AI agents need identity governance, not just secret storage

AI agents are not just another workload when they independently call tools and act at runtime. They may request credentials, use them in sequence, and move across systems in ways that static human-era IAM assumptions do not model well. If the agent is allowed to keep credentials for the duration of a task or longer, the organisation inherits machine-speed misuse risk without a corresponding governance boundary. The key issue is whether the identity layer can bind access to task context, revoke it automatically, and distinguish one agent action from the next.

Practical implication: govern AI agents as non-human identities with lifecycle, context, and revocation controls that reflect their operating speed.

Threat narrative

Attacker objective: The attacker’s objective is to use exposed machine credentials to move through connected financial systems with legitimate-looking access and extract value or data.

1. Entry occurs when machine credentials are stored in code, shared informally, or left unrotated long enough to be harvested or reused.
2. Credential access expands when a compromised secret lets an attacker authenticate as a bot, microservice, or third-party workload.
3. Escalation follows when that standing access reaches connected systems, cloud services, or partner environments that were never meant to be broadly reachable.
4. Impact lands in the form of credential-based breaches, regulatory fallout, and costly remediation across financial operations.

Breaches seen in the wild

• Azure Key Vault Contributor escalation 2024: Datadog found Azure Key Vault Contributor could add itself to access policies and read every secret, key and certificate in a vault.
• ASP.NET machine key attacks 2025: Developers copied ASP.NET machine keys from public sources; attackers used one to run Godzilla via ViewState. Microsoft found 3,000+ such keys.

Read and download The State of NHI & AI Agent Breach Report 2026, covering 150+ breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Machine identity sprawl is now a finance governance problem, not a vault problem: The article shows that credentials sit inside a much larger access model spanning APIs, pipelines, cloud systems, and AI-driven tools. Once machine identities outnumber humans by such a margin, the real issue becomes whether the organisation can inventory, scope, and revoke access across every runtime identity. Practitioners should treat secrets management as a governance function tied to lifecycle and entitlement control.

Standing secret exposure is the failure mode this article exposes: Secrets left in code, shared informally, or unrotated for months create the same structural weakness regardless of platform. That is not simply poor hygiene; it is persistent authentication exposure that turns every workload into a reusable entry point. The implication is that financial organisations must stop assuming secret storage equals secret control.

Ephemeral credential trust debt: The industry has accumulated a trust debt by designing for credentials that stay valid long enough to be managed manually. That assumption breaks when machine identities and AI agents operate at machine speed and can request, use, and discard access faster than human review cycles. The practitioner conclusion is that governance has to move from after-the-fact review to issuance-time control.

Finance needs lifecycle controls for non-human identities, not just encryption and audit logs: The article correctly connects compliance, risk, and operational speed, but the deeper lesson is that machine identities now require joiner-mover-leaver thinking. A credential that cannot be cleanly issued, rotated, and revoked on schedule is not just insecure, it is ungovernable in a regulated environment. Practitioners should align secrets management to identity lifecycle discipline.

AI agents inherit the same secrets problem but accelerate it: When an AI agent uses credentials to access systems and data, it creates a runtime identity boundary that human IAM controls were never built to police. The article’s strongest signal is that the same controls used for workloads must now extend to agentic behaviour, especially where task scope changes dynamically. Practitioners should evaluate whether their access model can distinguish one agent task from the next.

From our research library:

• Companies are dedicating an average of 32.4% of their security budgets to secrets management and code security, with US organisations leading at 40.8%, according to the State of Secrets in AppSec.
• 54% of organisations are dissatisfied with their current secrets management solution because not all secrets are secured, and 43% cite lack of central management, according to the 2024 State of Secrets Management Survey.
• Read next: Guide to the Secret Sprawl Challenge

What this signals

Ephemeral credential trust debt: Finance teams have spent years designing controls around secrets that stay valid long enough to be reviewed, rotated, and reported on later. That model no longer fits workloads and AI agents that request access at runtime and discard it before a human review cycle can even begin.

What changes next is the control boundary. Security programmes that still treat secrets as a storage problem will miss the real issue, which is issuance-time governance across machine identities, third-party access, and agentic workflows.

For practitioners

• Standardize machine identity inventory Map every application, bot, microservice, and AI agent that authenticates with a secret, then assign ownership and review cadence for each credential set.
• Replace standing secrets with task-scoped issuance Issue secrets only when a workload needs them, bind them to context and role, and expire them as soon as the task completes.
• Automate rotation and revocation Set rotation triggers for schedule, use, and suspected compromise, then verify revocation actually removes access from connected systems.
• Separate third-party credentials from internal trust paths Track vendor-owned machine access separately from internal workloads so offboarding, contract changes, and incident response can revoke access without delay.
• Govern AI agents as non-human identities Treat agent credentials as ephemeral, policy-driven access artifacts and require a revocation path that closes when the task closes.

Key takeaways

• Financial organisations now depend on machine identities and AI agents that expand the number of credentials security teams must govern.
• The biggest exposure is not only secret leakage but also long-lived, poorly scoped access that outlasts the business need for it.
• Controls that bind issuance, rotation, and revocation to runtime context are the difference between manageable machine access and hidden breach surface.

Key terms

• Machine Identity: The digital identity of a machine, device, or workload, such as a server, container, or VM, used to authenticate it within a network. Sometimes used interchangeably with NHI, though NHI is the broader category.
• Ephemeral Credentials: Ephemeral credentials are short-lived access artefacts issued for a limited task or session. They reduce the window for abuse, but they only improve security when paired with strong scope limits, telemetry, and automatic revocation at task completion.
• Secrets Sprawl: The uncontrolled proliferation of sensitive credentials, API keys, tokens, passwords, certificates, across codebases, cloud environments, CI/CD pipelines, and configuration files. In 2024, over 50 million leaked secrets were found on the dark web.
• Just-in-time secrets: Just-in-time secrets are credentials issued only when needed and for a short period. They reduce the time a secret exists and can be abused. In practice, a system generates or unlocks a token, password, or key at request time, binds it to a task or identity, and revokes it after use or expiry.

What's in the full article

Akeyless's full article covers the operational detail this post intentionally leaves for the source:

• How the zero-knowledge architecture and Distributed Fragments Cryptography model changes secret handling in regulated environments
• The finance-specific compliance mapping across PCI DSS, GLBA, NYDFS, SOX, and SEC expectations
• The on-demand identity model for AI agents and machine workloads, including ephemeral access patterns
• The vendor's operational claims about hybrid SaaS delivery and customer-controlled key fragments

👉 The full Akeyless article explains the compliance, architecture, and AI agent access model in more operational detail.

Deepen your knowledge

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