Data Center Execution Assurance

• Data Center Execution Assurance (DCEA) is a framework for end-to-end cryptographic attestation that binds static measurements to runtime state transitions in multi-tenant cloud systems.
• It utilizes layered intrusion management, formal threat and trust models, and segmented data partitioning to detect and contain malicious actions with measurable performance trade-offs.
• The approach integrates TEEs, TPMs, and CAIF to provide secure, auditable, and reversible execution controls, ensuring policy-driven recovery and evidence binding.

Data Center Execution Assurance (DCEA) encompasses a set of foundational mechanisms, formal models, and practical systems that collectively guarantee the correctness and integrity of computation in data center environments, even in the presence of malicious faults, advanced attacker capabilities, or non-deterministic workflows. Execution assurance—unlike narrow attestation or snapshot-based validation—demands robust, runtime, and end-to-end guarantees that every application-level action or control-plane mutation is authorized, evidentially bound, cryptographically protected, and operationally auditable in a multi-tenant, adversarial, and heterogeneous cloud setting.

1. Foundational Models: Threat, Trust, and Formalism

The formalization of DCEA is grounded in explicit adversary and trust models. Typical threat models admit that adversaries may control the host operating system, hypervisors, virtual TPMs, network infrastructure, and inject arbitrary software faults, but exclude physical attacks on sealed cryptographic hardware and trust only the vendor-supplied CPU/TEE/TPM vendor keys and measurement processes (Rezabek et al., 14 Oct 2025, Dyer et al., 2024).

In mathematical terms, assured execution is specified as the property that, for any process execution accepted by an external verifier, there exists a cryptographic chain of provenance—from static image hash and cryptographic measurement (e.g., PCRs, RTMRs, enclave report_data)—to runtime attestation of program state transitions (as evidenced by, e.g., control-flow graphs or observable hardware performance events). No code-paths, system state, or API mutations should escape this coverage except by system-parameterized, explicitly revocable exclusion governed by a policy engine (He et al., 10 Jun 2026, He et al., 18 Jun 2026).

2. Architectural Mechanisms and Intrusion Containment

A core pillar is active containment and recovery via layered runtime intrusion management. A canonical instantiation is the Adaptive Intrusion Management System (AIMS) in multi-tenant transactional databases, which classifies every transaction as benign ($t_b$), malicious ($t_m$), or affected ($t_a$) according to dependency relations: $$\mathrm{dep}(t_i, t_j) \Longleftrightarrow \exists o: o \in W(t_i) \wedge o \in R(t_j)$$ An affected transaction $t_a$ is any with a dependency path from a malicious $t_m$ or previously affected $t_a$ (Felemban et al., 2020). In the event of detection, the recovery subsystem locates all affected $t \in AT$ via dependency tracing, rolls back all objects to the last uncontaminated state, and re-executes the safe transaction chain.

To prevent propagation, data partitioning is re-engineered: every cross-tenant transaction on shared objects must span at least two physical nodes, enforcing a quarantine on commits until IDS approval, subject to hard performance constraints (minimizing

$$\sum_{t \in T} C(S_t),\quad \text{with} \quad |S_{t'}| > 1\ \forall t' \in T'$$

where $C(S_t)$ measures inter-node communication latency). This joint optimization yields a tunable containment–performance curve, with empirical reductions in affected transactions by up to 40% at the cost of up to 30% higher response time under aggressive partitioning (Felemban et al., 2020).

3. Platform Attestation, Binding, and Remote Proofs

Trusted Execution Environments (TEEs) and Confidential VMs are the substrate for assuring execution provenance. However, standard TEE attestation proves only the software state (“what code is running”), not physical provenance (“where” it is running), leading to attack surfaces such as Frankenstein proxies or replay-proxying (Rezabek et al., 14 Oct 2025).

The DCEA model in "Proof of Cloud" cryptographically binds each CVM's TEE evidence and a TPM-backed vTPM or hardware TPM anchor, leveraging the overlap between TEE (e.g., Intel TDX RTMRs) and TPM PCRs. For bare-metal deployments, Intel TXT extends measurements into TPM PCR 17–18, and the Attestation Key is sealed to these PCRs (Rezabek et al., 14 Oct 2025). A joint protocol ensures that the CVM's TDX quote and the TPM quote are produced on the same physical chassis, checked via:

• Equivalence of TDX's report_data to $t_m$0
• Full chain-of-trust from EKC $t_m$1 AK $t_m$2 TPM quote
• Cross-check of TDX RTMRs with TPM PCR values

Emergent DCEA protocols thus instantiate a dual-rooted cryptographic proof, closing the "location gap" without introducing new hardware or shifting trust beyond existing TPM/TEE primitives.

4. Runtime Control-Flow Attestation and Detection

Static attestation (launch-time) is insufficient for comprehensive DCEA, as runtime attacks (e.g., ROP, JOP, in-enclave exploits) can evade integrity checks post-boot (Morbitzer et al., 2022, Pott et al., 31 Mar 2026). Control-flow attestation (CFA) extends the assurance perimeter.

GuaranTEE leverages a dual-enclave design on Intel SGX—ProveTEE and VerifyTEE—where each basic block, return, or indirect branch is instrumented to output unique IDs into encrypted batches protected by a KDF and MAC chain. Batches are verified against a precomputed control-flow graph (CFG), and inconsistencies or tampering within or between batches are logged in an append-only attestation log. End-to-end, this attains detection of unauthorized control-flow transitions with single-digit percent overhead on standard cloud microservices, tunable via batch and feedback parameters (Morbitzer et al., 2022).

HPCCFA demonstrates a hardware-backed, sidecar-enclave approach for RISC-V TEEs, where observed hardware performance counter deltas, collected at ecall/timer boundaries, are verified against statically predicted vector cones (ILP feasibility over basic block paths and loop bodies). Detection probability scales as $t_m$3, balancing walkthrough overhead and detection confidence, with >99.9% detection achievable via moderate instrumentation density (Pott et al., 31 Mar 2026).

5. Certificate-Bound Authority and Runtime Enforcement

Execution assurance must address the control plane, especially under agentic or autonomous (AI-driven) workflows capable of requesting production mutations. The Sovereign Assurance Boundary (SAB) transforms ambient IAM into a regime of certificate-bound, evidence-bound, revocable, and auditable runtime authority (He et al., 10 Jun 2026). Each action is compiled into a contract $t_m$4, cryptographically bound to an evidence digest $t_m$5, routed for approval through validator/quorum/human-signature paths, and authorized only as a signed Sovereign Assurance Certificate ($t_m$6) scoped to identity, revocation epoch, and time window.

The Sovereign Execution Broker (SEB) is a trusted reference monitor and is the exclusive entity authorized—via deny-by-default policies in, e.g., AWS/K8s—to realize mutations. SEB verifies certificate authenticity ($t_m$7), action/parameter congruence, epoch and drift constraints, and prevents replay via nonce registration. Execution is contingent on passing the full predicate: $t_m$8 Ensuring only brokers can effect mutations guarantees agent proposals cannot bypass mandatory review and enforcement, while revocation and drift controls enable rapid "kill switch" responses to threats or policy changes (He et al., 18 Jun 2026).

6. Cryptographically Assured Information Flow and Formal Guarantees

Lower in the stack, minimality-oriented mechanisms such as Cryptographically Assured Information Flow (CAIF) enable assured remote execution even in the presence of arbitrary adversarial code (Dyer et al., 2024). A CAIF device is defined around an immutable per-device secret, hardware-resident key derivation, and primitives for attestation ("attest-locally"), verification ("check-attest"), confidential message exchange ("protect-for", "retrieve-from"), and secure channel bootstrapping. The CAIF ideal functionality $t_m$9 admits only those operations that can be provably traced to compliant, authenticated code images, yielding theoretical guarantees of provenance and confidentiality for microservices, secure multi-tenancy, and code update.

This formal security foundation, relying only on symmetric cryptography and internal key derivation, provides a post-quantum-resilient, composable substrate that can interoperate with or underpin TEE-based or TPM-based systems for higher-layer assurance objectives.

7. Performance, Trade-off, and Benchmarking

DCEA approaches are quantitatively characterized by explicit trade-offs between containment (or detection probability) and system-level performance. Key empirical findings include:

• Security-driven partitioning reduces transaction contamination and recovery times but increases average response times by up to 30% under maximal partitioning (Felemban et al., 2020).
• GuaranTEE achieves microservice-level attestation with sub-10% overhead with batching, but stress-tests on CPU-bound tasks show steep cost increases (Morbitzer et al., 2022).
• HPCCFA achieves 99.9% detection on cryptographic workloads with instrumentation, with runtime overhead scaling in proportion to measurement density (Pott et al., 31 Mar 2026).
• The SAB/SEB path introduces 28 ms median latency for K8s and 137 ms for AWS at 100% rejection of invalid/replayed requests (He et al., 18 Jun 2026).
• DCEA protocols for Confidential VMs achieve hardware-based attestation completion in 300–550 ms per call; these latency characteristics are operationally negligible for batch workflows but require careful design for interactive or high-throughput regimes (Rezabek et al., 14 Oct 2025).
• CAIF offers symmetric crypto operations at native hardware speed, trading away public-key agility for provable provenance (Dyer et al., 2024).

• Security and Performance Driven Architecture for Cloud Data Centers (Felemban et al., 2020)
• HPCCFA: Leveraging Hardware Performance Counters for Control Flow Attestation (Pott et al., 31 Mar 2026)
• TEE-BFT: Pricing the Security of Data Center Execution Assurance (Shamis et al., 30 Oct 2025)
• An End-to-End Assurance Framework for AI/ML Workloads in Datacenters (Gupta et al., 3 Jul 2025)
• Empowering Data Centers for Next Generation Trusted Computing (Dhar et al., 2022)
• Sovereign Assurance Boundary: Certificate-Bound Admission for Agentic Infrastructure (He et al., 10 Jun 2026)
• GuaranTEE: Introducing Control-Flow Attestation for Trusted Execution Environments (Morbitzer et al., 2022)
• Proof of Cloud: Data Center Execution Assurance for Confidential VMs (Rezabek et al., 14 Oct 2025)
• Cryptographically Assured Information Flow: Assured Remote Execution (Dyer et al., 2024)
• Sovereign Execution Brokers: Enforcing Certificate-Bound Authority in Agentic Control Planes (He et al., 18 Jun 2026)