AI security assessments expose where NHI controls still break down

At a glance

What this is: This is a threat-briefing and services overview that argues security teams need assessment, transformation, and rapid response across increasingly complex attack surfaces.

Why it matters: For IAM and NHI practitioners, it reinforces that identity governance must be tied to incident readiness, not treated as a separate policy exercise.

By the numbers:

• 200+ threat researchers inform the vendor's incident response and cyber risk management services.
• 30M malware samples per day analyzed support the vendor's threat research context.

👉 Read Palo Alto Networks' threat briefing on AI security assessments and incident response

Context

AI security assessments matter because autonomous systems and their supporting service accounts, tokens, and API keys expand the attack surface faster than many governance programmes can absorb. In NHI terms, the problem is not only whether credentials exist, but whether they are scoped, monitored, and recoverable when the surrounding workflow is under pressure.

Unit 42 positions its assessment, transformation, and response services as a single operating model for organisations trying to connect control testing, board communication, and incident handling. That framing is directionally correct for NHI governance: identity controls fail most often when they are not linked to detection and response. The starting point is typical for large enterprises with mature but fragmented security functions.

Unit 42 also highlights support across before, during, and after an incident, which reflects how NHI risk is operational rather than purely preventive. Once machine identities become part of production workflows, compromise paths can move from secret exposure to lateral abuse quickly, so response readiness becomes a governance requirement rather than a separate IR function.

Key questions

Q: How should security teams handle non-human identity risk in AI systems?

A: Treat every AI workflow as an identity problem first. Inventory the service accounts, API keys, tokens, and certificates behind the system, then reduce standing privilege, shorten credential lifetime, and correlate identity logs with response procedures. If you cannot revoke the right credential quickly, the environment is already too permissive.

Q: Why do AI systems make least privilege harder to enforce?

A: AI systems often chain multiple services, so a single credential can inherit broad downstream reach that was not obvious at design time. Least privilege becomes harder because access is distributed across orchestration, data, and automation layers. Teams need task-scoped permissions and continuous review, not static onboarding approvals.

Q: What is the difference between protecting a model and protecting its non-human identities?

A: Protecting a model focuses on the application or inference layer, while protecting non-human identities focuses on the credentials and permissions that let the system act. In practice, the second problem often creates the larger breach path, because tokens and service accounts can be reused, copied, or over-scoped across environments.

Q: When should organisations use zero standing privilege for machine identities?

A: Use zero standing privilege whenever a machine identity can reach production systems, sensitive data, or privileged automation. If the access is permanent, the breach window stays open. Ephemeral, task-scoped credentials reduce exposure and make revocation meaningful, especially in cloud and AI pipelines.

Technical breakdown

How AI security assessments expose NHI control gaps

AI security assessments are most useful when they test the full identity path around an AI system, not just the model or application layer. That means tracing service accounts, API keys, tokens, certificates, and delegated permissions across cloud, data, and orchestration layers. The technical failure mode is usually not a single broken control, but a chain of weak assumptions: over-scoped secrets, poor rotation, missing inventory, and inadequate segmentation. For NHI programmes, the assessment should ask whether the workload can still operate safely if one credential is exposed, duplicated, or reused elsewhere.

Practical implication: Map AI system access paths to the NHI lifecycle and test whether one compromised secret can reach more than one environment.

Why incident response for NHIs depends on identity telemetry

Incident response becomes much faster when teams can answer three questions in minutes: which non-human identities exist, what they can reach, and how they authenticate. Identity telemetry is the evidence layer that makes this possible. Without it, responders have to infer scope from logs that may not show the original secret source, the owning service, or the downstream permissions. That is especially risky in cloud and AI environments where credentials are ephemeral, copied into pipelines, or shared across automation jobs. The technical goal is to preserve attribution and containment paths before an attacker can reuse a token.

Practical implication: Ensure logs, secret inventories, and access reviews are correlated so responders can revoke the right credential first.

What zero trust means when the actor is a machine identity

Zero trust architecture assumes no implicit trust, but NHI implementation often violates that principle by granting persistent access to automation that is treated as operationally harmless. In practice, the control problem is not whether a machine identity is trusted, but how that trust is continuously revalidated. That requires short-lived credentials, explicit authorization boundaries, and policy checks that reflect workload context. When AI agents or service accounts can chain actions, the architecture must limit blast radius rather than depend on the original onboarding decision.

Practical implication: Apply continuous verification and least privilege to machine identities instead of inheriting standing access from deployment time.

Threat narrative

Attacker objective: The objective is to turn an overlooked machine identity into durable access across systems that were assumed to be protected by separate controls.

1. Entry occurs when attackers obtain exposed or reused service credentials that were never designed for human-style authentication scrutiny.
2. Escalation follows when those credentials carry broader permissions than the workload actually needs, allowing access to adjacent systems or data paths.
3. Impact comes when the attacker uses the compromised non-human identity to move through cloud, application, or AI workflows without triggering user-centric controls.

Breaches seen in the wild

• IOS app secrets leakage report — iOS apps leaking hardcoded secrets and credentials endangering user privacy.
• Cisco DevHub NHI breach — IntelBroker exploited exposed Cisco credentials, API tokens and keys in DevHub.

Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

AI security assessments are really NHI assessments once automation owns production access. The useful question is not whether a model is secure in isolation, but whether the identities around it are visible, scoped, and revocable. That shifts the centre of gravity from application hardening to identity blast radius control. Practitioners should treat AI security work as a test of NHI governance maturity.

Identity telemetry is the missing control plane for incident response in machine-heavy environments. If a security team cannot rapidly map a token, key, or certificate to an owner and a privilege scope, containment becomes guesswork. That is why NHI inventory, access logs, and rotation records need to be treated as response artifacts, not just compliance evidence. Practitioners should build those data paths before an incident forces the issue.

Zero standing privilege matters more for NHIs than for human accounts because machine access is easier to copy, reuse, and forget. Persistent access turns ordinary automation into latent risk, especially in cloud and AI pipelines. The discipline is to make access ephemeral, explicit, and bounded by task. Practitioners should re-evaluate any automation that still depends on durable secrets.

Threat-informed services are becoming the operating model for NHI resilience. The vendor's service stack reflects where the market is going: assessment, transformation, and response are converging because identity risk cannot be managed in quarterly review cycles alone. That does not replace governance, but it does validate the need for continuous validation. Practitioners should expect their NHI programme to look more like an operational control system than a policy document.

From our research:

• 72% of organisations have experienced or suspect they have experienced a breach of non-human identities, 46% confirmed and 26% suspected, according to The 2024 ESG Report: Managing Non-Human Identities.
• Enterprises that have experienced a compromised NHI averaged 2.7 separate incidents in the past 12 months, according to the same report.
• For a broader pattern of identity-driven compromise, see 52 NHI Breaches Analysis, which traces how weak lifecycle controls become repeatable attack paths.

What this signals

Identity blast radius is the right planning concept for AI-heavy environments. As more automation inherits production access, security teams should stop measuring risk by account count alone and start measuring how far one compromised identity can reach before containment. That framing aligns NHI governance with operational resilience and pairs naturally with NIST Cybersecurity Framework 2.0.

With 72% of organisations already reporting or suspecting an NHI breach, the issue is no longer whether machine identities matter but whether programme owners can prove control over them in time. The practical response is to integrate inventory, access review, and response playbooks into one operating rhythm, using NHI Lifecycle Management Guide as the process anchor.

Security teams should also prepare for AI-specific abuse patterns that resemble credential theft and workflow hijacking rather than classic account takeover. That makes token hygiene, task scoping, and revocation speed central design requirements, especially where agentic systems can act across multiple tools and data sources.

For practitioners

• Inventory every non-human identity tied to AI and automation Create a live register of service accounts, API keys, tokens, and certificates used by AI workflows, then map each to an owner, system, and privilege scope.
• Test blast radius with compromise scenarios Run compromise assessments against the credentials that matter most, and validate whether one exposed secret can reach adjacent cloud, data, or orchestration systems.
• Shorten credential lifetime and rotate on a schedule Move high-risk machine credentials toward short-lived issuance and enforce rotation for secrets that still support long-lived access.
• Make incident response identity-aware Correlate secret inventory, access logs, and change records so responders can revoke the correct non-human identity first and preserve service continuity.

Key takeaways

• AI security work becomes NHI governance work once automation carries real privileges across production systems.
• A compromised machine identity can create broader operational impact than a single user account because it is often less visible and more reusable.
• Security teams should treat identity telemetry, short-lived access, and blast-radius reduction as core controls, not optional hardening.

Key terms

• Non-Human Identity: A non-human identity is any digital identity used by software, services, workloads, or autonomous agents rather than people. In practice this includes service accounts, API keys, tokens, certificates, and AI agents. These identities often carry broad access and are easy to overlook in inventories and reviews.
• Identity Blast Radius: Identity blast radius is the amount of access, data, and systems a single credential can reach if it is compromised. It is a practical way to measure NHI risk because it shifts attention from account count to reachability, privilege depth, and how quickly access can be revoked.
• Zero Standing Privilege: Zero standing privilege means access is not permanently available. Credentials are issued only when needed, for a specific task, and then removed. For NHIs, this reduces the value of stolen secrets and limits how far an attacker can move if one automation identity is exposed.

Deepen your knowledge

AI security assessments and NHI lifecycle control are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are trying to connect AI governance with identity operations, it is worth exploring.

This post draws on content published by Palo Alto Networks: Unit 42 threat briefing and services overview. Read the original.