A few things that frame the scale:

• 64% of valid secrets leaked in 2022 are still valid and exploitable today, according to the Ultimate Guide to NHIs.
• Only 20% have formal processes for offboarding and revoking API keys, according to the Ultimate Guide to NHIs.

A question worth separating out:

Q: What should teams do when secrets are embedded in DevOps pipelines?

A: Treat embedded secrets as a lifecycle defect, not a storage issue. Remove hardcoded credentials, replace them with short-lived issuance paths, and ensure the pipeline can revoke access automatically when a leak or change occurs. The goal is to make the pipeline consume identity, not store it.

👉 Read our full editorial: Securing machine identities in financial DevOps environments

A few things that frame the scale:

• 91.6% of secrets remain valid five days after the targeted organisation is notified, showing a critical gap in remediation procedures, according to the Ultimate Guide to NHIs.
• Only 5.7% of organisations have full visibility into their service accounts, which is why lifecycle control and inventory discipline matter before incidents happen.

A question worth separating out:

Q: Should organisations prioritise secretless authentication before expanding multi-cloud use?

A: Yes, when workloads can authenticate through trusted identities instead of static secrets. Secretless patterns reduce the number of credentials that need rotation, lower the chance of secret sprawl, and make cross-cloud access easier to govern. They are especially valuable when teams already struggle with cloud-by-cloud lifecycle inconsistency.

👉 Read our full editorial: Multi-cloud secrets management exposes the NHI governance gap

TL;DR: Multi-cloud secrets management is hard because AWS, Azure, and GCP each handle credentials, versions, logging, and lifecycle differently, while compromised credentials account for 66% of attack scenarios, according to Akeyless. The governance problem is not just centralisation, but eliminating standing secrets, inconsistent rotation, and fragmented visibility across environments.

NHIMG editorial — based on content published by Akeyless: Multi-Cloud Secrets Management: Best Practices for Security and Efficiency

By the numbers:

• 66% of attack scenarios, contribute to 66% of attack scenarios, exposing businesses to potentially hundreds of breaches annually.
• Only 20% have formal processes for offboarding and revoking API keys, and even fewer have procedures for rotating them.

Questions worth separating out

Q: What breaks when cross-cloud access still depends on long-lived secrets?

A: Persistent secrets create reusable access that outlives the workload, the operator, and often the original business need.

Q: Why do long-lived secrets increase breach risk in cloud and fintech environments?

A: Long-lived secrets extend the time an attacker can reuse stolen access, which increases the chance of data theft, lateral movement, and compliance failure.

Q: How do security teams know if multi-cloud secret governance is failing?

A: Look for duplicated credentials, inconsistent rotation dates, secrets stored outside approved managers, and audit trails that differ by provider.

Practitioner guidance

• Centralise secret lifecycle policy Define one policy for creation, rotation, revocation, and recovery, then map it to each cloud’s native identity and secrets mechanism so the lifecycle is governed consistently.
• Replace long-lived secrets with ephemeral access Use just-in-time credentials and secretless authentication wherever workloads can authenticate through trusted identities such as cloud roles or federation.
• Unify audit trails across cloud providers Aggregate access logs and secret-use telemetry into one review path so incident responders can reconstruct who accessed which credential and when.

What's in the full article

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

• Step-by-step handling of AWS, Azure, and GCP authentication differences for secrets and workload identity
• Implementation detail for unified rotation, revocation, and version control across cloud-native secret stores
• How the Universal Secrets Connector works with existing vaults and cloud tools without forcing migration
• The article’s practical guidance on combining JIT credentials with CI/CD and infrastructure-as-code workflows

👉 Read Akeyless's guide to multi-cloud secrets management and NHI controls →

Multi-cloud secrets management: is your NHI governance keeping up?

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TL;DR: Credential-related breaches are now the leading cause of cybersecurity incidents in financial services, and Akeyless argues that machine identities, not human users, drive most of the exposure. The central problem is not secrets storage alone but standing access, slow rotation, and governance models that cannot keep pace with DevOps scale.

NHIMG editorial — based on content published by Akeyless: A DevOps-First Guide to Securing Machine Identities in a High-Risk, Regulated World

By the numbers:

• According to the IBM Cost of a Data Breach Report (2024), the average cost of a breach in financial services is over $6 million.
• For every human user in the environment, there are at least 45 machine identities.
• 85% of identity-related breaches are tied to machine, chine identities.

Questions worth separating out

Q: What breaks when organisations keep using standing privileges for machine identities?

A: Standing privilege turns NHIs into persistent trust anchors.

Q: Why do financial services need to prioritise machine identity governance?

A: Financial environments depend on large numbers of non-human credentials to move data and execute transactions at machine speed.

Q: How do security teams know whether secret rotation is actually working?

A: Rotation is working only if exposed credentials are found quickly, revoked everywhere they are used and replaced before attackers can reuse them.

Practitioner guidance

• Centralise machine credential inventory Build a single inventory for API keys, certificates, tokens, and service accounts across code, CI/CD, cloud, and collaboration tools.
• Replace persistent access with JIT issuance Use temporary, policy-bound secrets for privileged machine tasks and expire them automatically after completion.
• Automate revocation across downstream systems Trigger secret invalidation the moment a leak, misuse, or role change is detected, then propagate updates to databases, containers, APIs, and pipeline variables without manual intervention.

What's in the full article

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

• Step-by-step secrets management patterns for DevOps and financial services pipelines
• Detailed compliance mapping for PCI DSS v4.0, GLBA, NYDFS, SOX, and SEC expectations
• Implementation notes for DFC, zero-knowledge architecture, and hybrid deployment models
• Product-specific integration guidance for GitHub Actions, GitLab, Jenkins, Terraform, IAM roles, and OIDC

👉 Read Akeyless's analysis of machine identity security in financial DevOps →

Machine identities in DevOps: what financial teams need now?

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A question worth separating out:

Q: What is the main governance risk in consumer AI privacy settings?

A: The main risk is policy drift, where users assume a tool is private because it feels private, but the actual data path includes retention, training options, or third-party model access. That mismatch creates shadow AI exposure and weakens any attempt to govern sensitive work consistently.

👉 Read our full editorial: Venice.ai and Claude privacy tradeoffs for individual AI use

TL;DR: The default Kimi K2.5 path avoids conversation logging and model training, while Claude’s consumer plans require an explicit training choice and retain chats under Anthropic’s policy, according to Venice.ai. The governance issue is not just privacy preference but where identity, retention, and upstream model visibility create control boundaries for individual AI use.

NHIMG editorial — based on content published by Venice.ai: Claude vs Venice.ai privacy and model-access comparison

By the numbers:

• Only 5.7% of organisations have full visibility into their service accounts.
• 79% of organisations have experienced secrets leaks, with 77% of these incidents resulting in tangible damage.

Questions worth separating out

Q: How should organisations govern consumer AI tools that offer no-log defaults?

A: Treat retention, training, and identity handling as separate controls.

Q: Why does anonymous access to a model not guarantee privacy?

A: Because anonymity only removes or masks the user identity, not the prompt content.

Q: When should teams prefer enterprise AI contracts over consumer chat tools?

A: Use enterprise contracts when the conversation includes business data, regulated information, client material, or anything that needs auditability and predictable retention.

Practitioner guidance

• Define approved consumer AI privacy tiers Classify tools by no-log defaults, account retention, training choice, and upstream model disclosure before allowing any sensitive use.
• Block sensitive prompts from identity-light tools Prohibit regulated, confidential, or customer data from tools that route content through third-party models or preserve account-linked history.
• Separate personal use from enterprise workflow Publish a clear rule that consumer privacy settings do not make a tool suitable for work-team processing, procurement, or record retention.

What's in the full article

Venice.ai's full article covers the operational detail this post intentionally leaves for the source:

• Side-by-side feature and pricing differences across Venice.ai and Claude for individuals and teams
• Model-specific handling details for Claude, Grok, and OpenAI routing through Venice's anonymizing proxy
• Practical setup guidance for importing memory, switching models, and keeping chat history local
• Plan-level guidance for choosing between consumer, team, and API usage paths

👉 Read Venice.ai's comparison of privacy, retention, and model access in Claude vs Venice →

Claude vs Venice.ai: which privacy posture fits individual users?

Explore further

View Full Forum →  |  NHI Foundation Course →

A few things that frame the scale:

• 71% of NHIs are not rotated within recommended time frames, increasing the risk of compromise over time, according to the Ultimate Guide to NHIs.
• Only 5.7% of organisations have full visibility into their service accounts, which is why inherited secrets platforms need governance across users, keys, and endpoints.

A question worth separating out:

Q: How do you know whether endpoint controls are strong enough for a secrets manager?

A: Look for patched devices, controlled browser extensions, enforced disk encryption, endpoint protection, and limited local administrator rights on every machine that can decrypt secrets. If any one of those controls is missing, the endpoint remains a likely path to credential exposure.

👉 Read our full editorial: Passbolt self-hosted security check-up for inherited admin access

A question worth separating out:

Q: How do identity and AppSec teams share accountability for spec-driven development security?

A: AppSec owns the control checks on the implementation, but identity teams own the correctness of role and access assumptions embedded in the design. The strongest model is shared accountability at design time, where entitlement scope, privileged workflows, and data exposure are reviewed together. That prevents access decisions from being hidden inside a development ticket.

👉 Read our full editorial: Spec-driven development security is now the new attack surface

A question worth separating out:

Q: What should teams do immediately after a major architecture or M&A change?

A: Rebuild scope, refresh the asset inventory, and rescore the highest-risk attack paths before the new environment settles into routine operations. Those events can alter data flows, trust boundaries, and privileged access patterns overnight, so waiting for the next planned review leaves a blind spot.

👉 Read our full editorial: Threat assessments are failing because scan volume hides breach paths

A question worth separating out:

Q: How should security teams compare embedded AI controls with a horizontal governance layer?

A: Embedded controls work inside a single platform, but a horizontal governance layer is needed when agents move across clouds, identity systems, and data environments. Security teams should compare them on coverage, not vendor feature lists. If policy cannot follow the agent across workflows, the control model is incomplete.

👉 Read our full editorial: Guardian agents and AI governance: why oversight must scale