Chainlink cracks the $3.4 trillion dilemma! Confidential computing unlocks Wall Street get on board

Banks do not disclose their risk exposures, and asset management companies do not reveal their clients' investment portfolios, but they all seek to achieve programmable settlement and verifiable execution. This tension has kept institutional capital hovering on the edge of the public blockchain; if banks cannot enter the public blockchain market under confidentiality, then the entire $3.4 trillion crypto assets market effectively remains closed.

The Technical Architecture and Innovation of Chainlink's Confidential Computing

Chainlink believes it can bridge the gap between institutions and public Blockchains through “confidential computing.” “Confidential computing” is a privacy layer in its new Chainlink runtime environment that processes sensitive data off-chain and returns verified results on-chain, without ever revealing inputs or logic to the public ledger. The service was launched on November 4 as part of CRE, with plans for early access in 2026 and wider promotion later that year.

The initial workflow runs in a cloud-hosted Trusted Execution Environment (TEE), which are isolated hardware environments that can execute code without exposing data to the host system. Microsoft defines TEE as hardware capable of isolating the execution of code and data, providing strong confidentiality and near-native speed without the overhead of encryption. Product-market fit refers to the situation where the funding system cannot wait several minutes to generate proof when collateral needs to be moved within seconds.

The published roadmap supports technologies such as zero-knowledge proofs, multi-party computation, and fully homomorphic encryption. As these technologies mature, they will also be developed. Chainlink has also revealed two subsystems specifically constructed for institutional use cases: a decentralized key generation system for session keys and a “Vault DON” for decentralized storage of long-term confidential data. They seem to believe that this is how tokenized assets, cross-chain delivery and payments, and compliance checks can be achieved without exposing holdings, counterparties, or API credentials to the public memory pool.

Bank-grade data meets verifiable execution standards

The short-term value is evident. Institutions can use proprietary data or external data sources on the Blockchain without disclosing the original information. Chainlink's examples cover tokenizing private real-world assets, distributing confidential data to paying subscribers, delivery and payment across public and permissioned chains, and returning binary “yes” or “no” attributes for KYC or qualification checks on the Blockchain while retaining an audit trail for regulators.

Each workflow in CRE generates an encryption proof that records the logic executed and its runtime, but does not include underlying data or business rules. This structure is important for two reasons. First, it separates the verification layer from the data layer, allowing auditors or counterparties to confirm execution integrity without viewing sensitive inputs. Second, it can operate through a single coordination point between public chains, permissioned networks, and Web2 APIs.

For financial departments managing the liquidity of collateral or tokenized platforms distributing compliance assets, this means a single integration without the need to customize bridges for each environment. This “one integration, multi-chain deployment” architecture significantly reduces the technical barriers and maintenance costs for institutions.

Chainlink Confidential Computing's Enterprise-Level Application Scenarios

Private RWA Tokenization: On-chain trading of sensitive assets such as real estate and private equity

Confidential Data Distribution: Paid subscription services for financial data, credit scoring, etc.

Cross-chain Privacy Settlement: Confidential delivery and payment between public chains and permissioned chains.

Compliance Check: KYC and qualification verification return “Yes/No” results without disclosing identity information.

Competition of TEE with ZK and FHE Technical Routes

Today, privacy technologies are divided into three design concepts, each with different trade-offs in terms of performance, trust assumptions, and maturity. Privacy aggregation (e.g., Aztec) maintains the privacy of transactions and states at the encryption level using zero-knowledge proofs. All data remains encrypted, but the verification costs are high, and cross-chain composability requires the use of bridge protocols. Aztec's privacy protection solution has been launched on the public testnet in May, while applications with privacy protection enabled by default have already gone live with the release of Aleo.

Confidential EVM layers such as fhEVM from Fhenix, Inco, and Zama utilize fully homomorphic encryption, allowing users to compute directly on encrypted data. However, FHE remains the most expensive option, and the related tools are still under development and refinement. Confidential EVMs based on TEE, such as Oasis Sapphire, achieve native execution speed by isolating code within hardware enclaves.

Chainlink's confidential computing service started from the TEE camp, as institutions currently need this kind of performance. However, Chainlink realized that the TEE trust model causes some users to feel concerned, so CRE has implemented decentralized proof and secret sharing for execution within its oracle network, and the roadmap clearly includes ZK, MPC, and FHE backends.

Recent research has shown that new attack methods targeting Intel SGX secure enclaves are emerging one after another, including physical intermediary technology. CRE's decentralized oracle network proof and distributed key management aim to mitigate this risk: no single TEE holds all the secrets, and the encrypted log creates an audit trail that can be preserved even if the secure enclave is compromised.

Strategic Trade-offs between Privacy and Liquidity

Choosing to structure privacy as an off-chain service rather than a separate chain creates a unique combinatorial feature compared to privacy aggregation. If private RWA tokens and confidential data streams are routed through CRE, they will still settle on public Ethereum, Base, or permissioned chains, where liquidity already exists. This means that privacy protection workflows can utilize the same collateral pools and DeFi primitives as open applications, with the only difference being that sensitive fields are protected.

Privacy aggregators provide stronger encryption guarantees, but they isolate liquidity within their own execution environments, requiring bridges to interact with the wider ecosystem. For institutions weighing whether to tokenize on a privacy L2 or conduct confidential computing on Ethereum, the question becomes: do users value encryption privacy or interoperability more, or do they prioritize speed and connectivity over provable encryption?

Timing is crucial. The confidential computing plan will be delivered to early users in 2026 rather than today. If institutions believe they need encryption privacy protection and can tolerate issues like slightly slower performance or liquidity isolation, then these alternatives will be production-ready when CRE early access begins. If institutions prioritize speed, auditability, and the ability to integrate with existing Web2 and multi-chain infrastructure, then Chainlink's TEE priority approach may gain traction in the short term before ZK and FHE mature.

The answer depends on who takes action first: the banks that need privacy for transactions or the cryptographers who want to eliminate hardware trust. Chainlink bets that it can first meet the needs of the former, while the latter will catch up.