Why do just-in-time models fail during major disruptions?

Why This Matters for Security Teams

Just-in-time models are designed to reduce waste, but the same lack of slack becomes a security and resilience problem when conditions change faster than the operating model can respond. In procurement, logistics, and access governance alike, the system assumes normal lead times, stable dependencies, and predictable demand. When those assumptions break, teams lose the ability to absorb variance without visible impact.

For NHI and secrets management, the parallel is direct. If access, rotation, or supply assumptions are too tightly coupled to routine operations, disruption exposes hidden dependencies immediately. NHI Management Group has documented how rapid attacker behaviour can exploit exposed credentials within minutes in the LLMjacking: How Attackers Hijack AI Using Compromised NHIs research, which is a useful reminder that time-to-action matters as much as time-to-recover. Security teams should also distinguish operational efficiency from resilience; the NIST Cybersecurity Framework 2.0 frames this as a governance problem as much as a technical one.

In practice, many security teams encounter the real cost of JIT only after a disruption has already interrupted production, access, or incident response rather than through intentional resilience testing.

How It Works in Practice

JIT models fail during major disruptions because they depend on fast replenishment, stable networks, and a narrow tolerance for delay. In normal conditions, inventory or credentials arrive only when needed, which reduces carrying cost and exposure. During a disruption, however, those same optimisations create a single-point timing dependency: if transport stalls, a supplier is compromised, a regulator changes requirements, or a system outage delays approval, there is no reserve to bridge the gap.

For security practitioners, the same pattern appears in secrets and NHI workflows. Long-lived approvals, tightly synchronized rotations, and assumed availability of upstream systems can all become failure amplifiers. When teams postpone rotation because the environment is busy, or when tooling requires too many synchronous steps to issue access, the organisation has effectively traded resilience for efficiency. NHI Management Group’s Guide to NHI Rotation Challenges is a useful reference for why rotation becomes brittle when every dependency must be online at the same moment.

• Buffering can be physical inventory, extra approval capacity, staged credential rollout, or fallback suppliers.
• Detection matters: disruption is easier to absorb when teams can see which dependencies are critical before they fail.
• Segmentation matters: one delayed input should not halt all downstream workflows.
• Recovery design matters: manual override paths and pre-approved contingencies reduce the blast radius.

Best practice is evolving toward selective slack, not universal stockpiling, because modern environments still need cost control and traceability. These controls tend to break down when a just-in-time process depends on a single external system for every approval, delivery, or rotation step because the failure becomes synchronized across the entire chain.

Common Variations and Edge Cases

Tighter just-in-time controls often reduce waste and exposure, but they also increase fragility, so organisations must balance efficiency against continuity. That tradeoff is especially visible in regulated sectors, cross-border supply chains, and highly automated security operations.

One common edge case is the difference between predictable spikes and true disruptions. JIT can handle forecastable demand if the organisation has reliable signals and alternate routing, but it performs poorly when the disruption is systemic, such as regional transport loss, a supplier insolvency, a mass credential revocation event, or a broad cloud service outage. In those cases, the issue is not volume, but the inability to replenish at all.

Another variation is temporary overcorrection. Some organisations respond to disruption by creating too much buffer, which can introduce new cost, sprawl, and stale access. Guidance for this area is still maturing, but current guidance suggests focusing on risk-based reserves, short-lived exceptions, and clearly tested fallback processes instead of abandoning JIT entirely. For broader resilience and governance alignment, the operational framing in NIST Cybersecurity Framework 2.0 remains relevant, while the DeepSeek breach demonstrates how hidden dependencies and exposed data can turn a routine operating model into an enterprise-wide incident.

In practice, JIT is weakest where recovery depends on the same infrastructure that failed in the first place.

Related resources from NHI Mgmt Group

• Why do single-DNS setups fail during major audience surges?
• Why do point-in-time reviews fail for shadow AI?
• Why do backups fail during ransomware incidents even when they exist?
• Why do AI security controls often fail to transfer across deployment models?