Tokenized finance needs chain-specific governance, not one blockchain

At a glance

What this is: Chainalysis argues that tokenized finance infrastructure should be chosen by use case, with different networks optimising for different combinations of fees, finality, liquidity, and compliance risk.

Why it matters: For IAM and security teams supporting digital asset platforms, the article shows that operational trust boundaries shift with the rail, so governance, monitoring, and custody controls must be matched to the selected network.

By the numbers:

• Bitcoin carries a kurtosis of 246, likely related to fee expansion during the runes and ordinal mania.
• Arbitrum consistently holds the top spot for fastest block completion, with the exception of April 2025.

👉 Read Chainalysis's report on how digital assets are reshaping finance infrastructure

Context

Tokenized finance is moving from pilot activity to live deployments, which means infrastructure choices now shape settlement risk, compliance overhead, and custody exposure. The core question is not whether blockchain can support financial assets, but which network characteristics fit the asset class, operating model, and control requirements.

The article also has an identity-adjacent governance angle: when institutions build on public networks, they depend on access control, wallet oversight, and monitoring of who can move value and under what conditions. That makes the topic relevant to IAM, PAM, and controls around machine and service identities inside financial platforms.

Key questions

Q: How should institutions choose a blockchain for tokenized assets?

A: They should choose by asset class and control requirement, not by brand recognition or raw speed. The right rail depends on whether the asset needs low cost volatility, high throughput, hard settlement finality, or stronger compliance visibility. A defensible choice starts with the business risk being managed and ends with controls that match that risk profile.

Q: Why does finality matter more than throughput for high-value settlement?

A: Throughput only tells you how much a network can process, while finality tells you when a transaction is truly irreversible. For high-value settlement, provisional acceptance is not enough, because the institution still carries economic and operational exposure until settlement is final. That is why finality should drive governance for regulated assets.

Q: What do security and IAM teams get wrong about custody concentration?

A: They often treat liquidity concentration as a market issue rather than a governance issue. In practice, concentration creates hidden trust dependencies, because a failure at one exchange or custodian can cascade into withdrawal freezes, pricing stress, and confidence loss. The control response is dependency mapping, not just incident response.

Q: Who is accountable when monitoring gaps allow illicit exposure to grow?

A: Accountability sits with the institution operating the asset programme, not with the blockchain network itself. If the organisation chooses a rail with meaningful exposure, it must pair that choice with screening, alerting, and escalation controls that scale with volume. Governance fails when monitoring is treated as optional once the network is live.

Technical breakdown

Transaction fees and tail risk in tokenized finance

For financial institutions, average fees matter less than fee predictability. A network with low baseline costs can still be operationally risky if congestion causes sharp price spikes, because batch settlement, rebalancing, and treasury flows depend on stable execution costs. Tail risk shows up when the same rail can be cheap on most days and suddenly expensive under load. That makes fee kurtosis a more useful planning signal than a simple average, especially for regulated workflows that must run on fixed schedules and at scale.

Practical implication: model fee volatility, not just average cost, before assigning payment or settlement workloads to a network.

Throughput, soft finality, and true settlement

Throughput measures how many transactions a network can process, while finality measures when those transactions become irreversible. The distinction matters because a system can look fast operationally while still carrying settlement delay risk underneath, especially on L2s that must later anchor activity back to a base chain. For high-value assets, soft finality is not enough if the business process requires hard settlement certainty. Institutions need to know where provisional acceptance ends and legal or economic finality begins.

Practical implication: separate business acceptance from final settlement in your control design and escalation procedures.

Custody concentration and contagion risk in digital asset networks

A network can be technically resilient while still being operationally fragile if custody is concentrated in too few entities. When one exchange dominates a token’s custody or liquidity flows, a hack, run, or insolvency can propagate across the ecosystem as a systemic shock. This is a governance problem as much as a market structure problem, because dependency chains create implicit trust relationships that are not always visible in tooling or contracts. The risk is amplification, not just loss at a single point.

Practical implication: map concentration exposures across counterparties, custody providers, and liquidity dependencies before assuming network resilience.

Threat narrative

Attacker objective: The objective is to exploit concentrated dependency so that a failure or compromise at one trusted actor destabilises a wider market or custody network.

1. Entry occurs through concentration in a dominant exchange or custody provider, where a network appears healthy but depends on a narrow set of trusted actors.
2. Escalation follows when a hack, run, or insolvency at that node spreads stress through shared liquidity and custody dependencies.
3. Impact is systemic, because a failure at one provider can freeze withdrawals, distort pricing, and trigger broader confidence loss across the ecosystem.

NHI Mgmt Group analysis

Blockchain selection is now an infrastructure governance decision, not a technology preference. The article shows that institutions cannot optimise for every dimension at once, because cost, finality, throughput, and compliance pull in different directions. That is the same kind of trade-off analysis identity teams make when they distinguish between authentication strength, access boundaries, and operational control. Practitioners should treat chain choice as a control-design problem, not a procurement slogan.

The most useful concept here is network fit, not network supremacy. A settlement rail that works for a money market fund may be wrong for high-frequency trading, and vice versa. That means architecture reviews should ask which risk is being accepted, where the operational bottleneck sits, and which monitoring layer is compensating for it. For practitioners, the conclusion is simple: match the rail to the asset, then govern the residual risk explicitly.

Custody concentration is a hidden control failure because it creates a trust dependency that looks like market liquidity. The article correctly frames this as a systemic risk rather than a narrow exchange issue. For IAM and PAM teams, the parallel is clear: when too much value or privilege is concentrated in too few accounts or providers, resilience depends on assumptions that are easy to overlook. Practitioners should inventory those dependencies before they become the failure path.

Compliance tooling has to scale with exposure, not with optimism. The article’s point about illicit activity and monitoring is that visibility is a prerequisite for operating across multiple networks responsibly. That aligns with how identity programmes treat privileged activity: if you cannot observe movement, screening, or approval flows, you cannot govern them. The practitioner takeaway is to align monitoring depth with the actual exposure profile of the rail.

What this signals

Trust concentration is the central governance pattern to watch as tokenized finance matures. The same way identity programmes fail when too much privilege sits in too few accounts, digital asset platforms fail when custody, liquidity, or execution dependency narrows to a small set of actors. That makes concentration analysis a practical governance discipline, not an abstract market concern.

For practitioners, the immediate question is whether the selected rail can support both operational scale and observability. Chain choice should be paired with controls that track counterparties, settlement status, and exception handling, because the market will not forgive control gaps that only appear once value is moving.

When institutions expand into tokenized assets, the security conversation should include who can move value, who can reverse it, and which monitoring systems can detect abnormal movement early. That is where identity, privilege, and transaction governance intersect in a way that looks very similar to other high-trust digital systems.

For practitioners

• Map asset-specific control requirements Define which assets require low fees, which require hard finality, and which can tolerate soft settlement before selecting a chain. Tie each use case to explicit operating thresholds so the rail is chosen for the asset, not the other way around.
• Model fee volatility as an operational risk Measure not only average transaction cost but also congestion spikes, batch execution costs, and treasury impact during peak demand. Use those results to set approval thresholds and contingency paths for high-volume operations.
• Inventory custody and liquidity dependencies Identify exchanges, wallets, intermediaries, and providers whose failure could cascade across your tokenized asset programme. Include concentration at the level of counterparties and execution venues, not just direct custodians.
• Strengthen monitoring for illicit exposure Apply continuous screening to wallet activity, counterparties, and transaction flows so compliance teams can see exposure before it accumulates. Use monitoring as an operational control, not a retrospective reporting layer.

Key takeaways

• Tokenized finance infrastructure should be selected by asset-specific risk, not by generic blockchain preference.
• Fee volatility, finality, and custody concentration are operational controls as much as technical properties.
• Institutions need monitoring and dependency mapping that scale with exposure if they want digital asset programmes to remain governable.

Key terms

• Finality: Finality is the point at which a transaction becomes irreversible and can be treated as settled. In tokenized finance, finality matters because a transaction can look accepted before the underlying network has fully committed it, creating residual settlement and operational risk.
• Fee Tail Risk: Fee tail risk is the chance that network costs spike sharply during congestion even when day-to-day fees look low. For regulated workloads, the issue is not average price but whether the rail can sustain predictable cost under stress without breaking operations or approval thresholds.
• Custody Concentration: Custody concentration occurs when too much asset control or liquidity depends on a small number of exchanges, custodians, or intermediaries. It is a governance risk because the failure of one trusted actor can cascade into broader market disruption, even if the underlying chain remains technically healthy.
• Soft Finality: Soft finality is provisional confirmation that a transaction has been ordered or accepted by the network but not yet irreversibly settled. It can be sufficient for low-risk activity, but it is not the same as hard settlement and should not be treated as equivalent for high-value assets.

What's in the full report

Chainalysis's full report covers the operational detail this post intentionally leaves for the source:

• The full network-by-network trade-off analysis across cost, throughput, finality, contagion risk, illicit exposure, and governance.
• The underlying methodology for the radar charts, fee kurtosis, and dependency measures used to compare chains.
• The asset-class-specific examples for institutional tokenization, including how different rails support different business models.
• The broader market framing for stablecoin growth and why infrastructure decisions now affect long-term operating strategy.

👉 The full Chainalysis report includes the network comparisons, methodology, and institutional use-case detail behind the framework.

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The NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, workload identity, and secrets management for practitioners responsible for access boundaries. It gives security teams a practical way to connect identity control to the broader systems they govern.