Life Cycle Assessment

Life cycle assessment is a method for estimating the environmental impact of a product or system from design through disposal. In AI, it helps teams compare energy use, material demand, repairability, and recyclability so sustainability decisions are based on the full operational footprint, not just model performance.

Expanded Definition

Life cycle assessment, or LCA, evaluates environmental impact across every stage of a product or system, from raw material extraction and design through deployment, maintenance, and end-of-life disposal. In AI operations, the term is often applied to compare not only training and inference energy use, but also hardware sourcing, cooling demands, replacement cycles, and recyclability. That broader view matters because sustainability claims can look favorable at model level while the underlying infrastructure still creates substantial emissions or waste.

For NHI and agentic AI governance, LCA is most useful when it is tied to procurement and runtime decisions, not treated as a one-time sustainability exercise. Teams can use it to compare different hosting models, device fleets, or secret storage patterns when those choices affect equipment churn and operational overhead. Definitions vary across vendors when LCA is folded into carbon accounting, so practitioners should distinguish a full life cycle method from simple energy reporting. For the governance side of AI systems, the OWASP Non-Human Identity Top 10 is a useful complement because lifecycle decisions often overlap with NHI exposure, rotation, and offboarding. The most common misapplication is using LCA to justify a model choice based only on training emissions, which occurs when teams ignore downstream hardware, storage, and disposal impacts.

Examples and Use Cases

Implementing life cycle assessment rigorously often introduces data-collection overhead and slower decision-making, requiring organisations to weigh sustainability insight against reporting cost.

• A team compares two AI deployment patterns and chooses the option with lower total energy demand across training, inference, and scheduled retraining, rather than just the smallest benchmarked model.
• Procurement evaluates whether a dedicated accelerator fleet or shared infrastructure creates less long-term material waste, using the Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs as a governance reference for operational lifecycle thinking.
• Security and sustainability teams review secret storage patterns because repeated device rebuilds and manual credential replacement can increase operational churn, aligning with guidance in the Guide to the Secret Sprawl Challenge.
• A product owner examines end-of-life disposal for edge devices running AI agents, including battery replacement, board recyclability, and secure decommissioning of embedded identities.
• An architecture review compares centralised versus distributed model hosting, using the OWASP Non-Human Identity Top 10 to account for lifecycle exposure as identities move through build, operate, and retire phases.

NHIMG’s research shows that 96% of organisations store secrets outside secrets managers in vulnerable locations, which can create avoidable rework, incident recovery, and hardware refresh pressure when systems must be rebuilt or rotated urgently. That makes LCA relevant not only to emissions reporting but also to operational resilience and asset planning.

Why It Matters in NHI Security

Life cycle assessment matters in NHI security because insecure or short-lived operational choices often create hidden environmental and governance costs. A platform that looks efficient at launch can still generate waste if credentials are hardcoded, devices are discarded prematurely, or service accounts require repeated rebuilds after incidents. That is where LCA intersects with NHI lifecycle management: it forces teams to account for the full cost of rotation, revocation, replacement, and disposal instead of treating those activities as separate operational chores.

NHIMG reports that only 20% of organisations have formal processes for offboarding and revoking API keys, and even fewer have procedures for rotating them, which means many environments absorb unnecessary remediation churn. This is why LCA should be read alongside the NHI Lifecycle Management Guide and the Guide to NHI Rotation Challenges: the sustainability of an AI stack depends on how cleanly its identities can be managed over time. Organisations typically encounter the true lifecycle cost only after a breach, forced rotation, or hardware refresh, at which point life cycle assessment becomes operationally unavoidable to address.

Related resources from NHI Mgmt Group

• When should organisations prioritise supplier recertification over a new assessment cycle?
• Data Life-Cycle Management
• Software Development Life Cycle
• How should organisations include identity risk in GRC risk assessment?