Confidential Computing protects data in use—the third and historically most vulnerable state of the data lifecycle—by encrypting it within a CPU-enforced Trusted Execution Environment (TEE). Unlike encryption for data at rest or in transit, this hardware-isolated enclave prevents even a compromised operating system, hypervisor, or cloud administrator from accessing the code or data being processed, ensuring runtime confidentiality and integrity.
Glossary
Confidential Computing

What is Confidential Computing?
Confidential Computing is a hardware-based security paradigm that protects data in use by performing computation within a hardware-based Trusted Execution Environment (TEE), isolating sensitive workloads from the host operating system, hypervisor, and cloud infrastructure providers.
The TEE provides cryptographic attestation, a mechanism that verifies to a remote party that the correct, untampered code is running inside the enclave before any secrets are released. This enables multi-party data collaboration scenarios—such as federated analytics across competing organizations—where sensitive datasets can be jointly processed without any party exposing raw data to another, satisfying strict data sovereignty and regulatory requirements.
Core Properties of Confidential Computing
Confidential Computing protects data in use—during active computation—by isolating it within a hardware-based Trusted Execution Environment (TEE). This prevents unauthorized access even from the host operating system, hypervisor, or cloud provider administrators.
Hardware-Grade Memory Isolation
The CPU creates a private, encrypted enclave within main memory that is inaccessible to all other software, including the OS kernel and hypervisor.
- Enclave Page Cache (EPC): A dedicated, encrypted memory region reserved exclusively for TEE operations
- Hardware-enforced boundaries: Any attempt by non-enclave code to read enclave memory triggers a processor-level access violation
- Example: Intel SGX allocates up to 512MB of private memory per enclave, while AMD SEV encrypts entire virtual machines with per-VM keys
Runtime Data Encryption
Unlike traditional encryption that protects data at rest (storage) and in transit (network), Confidential Computing encrypts data in use within the CPU itself.
- Transparent encryption engine: A dedicated on-die memory controller automatically encrypts and decrypts data as it moves between cache and RAM
- Per-enclave encryption keys: Each TEE instance gets unique keys, preventing cross-enclave data leakage
- Example: AMD SEV-ES encrypts all CPU register state when a VM stops, preventing the hypervisor from inspecting guest register values
Remote Attestation
A cryptographic mechanism that allows a remote party to verify that the correct, untampered code is running inside a genuine TEE before sending secrets.
- Hardware-signed measurement: The CPU generates a cryptographic hash of the enclave's initial code and data, signed by a key fused into the processor at manufacture
- Attestation report: Contains the enclave's identity, code measurement, and TCB version, verifiable against the hardware vendor's public key infrastructure
- Example: Intel's Enhanced Privacy ID (EPID) allows anonymous attestation without revealing the specific processor's identity
Minimal Trusted Computing Base (TCB)
Confidential Computing radically reduces the attack surface by excluding the operating system, hypervisor, and cloud provider from the trust boundary.
- TCB components: Only the application code, the TEE firmware, and the processor itself must be trusted
- Excluded from TCB: Host OS, device drivers, orchestration layers, and cloud administrator tools
- Security implication: Even a fully compromised host OS cannot read enclave memory or tamper with computation
- Example: AWS Nitro Enclaves have no persistent storage, no interactive access, and no external networking—only a vsock for secure local communication
Data-in-Use Integrity Protection
Beyond confidentiality, TEEs provide cryptographic integrity guarantees that prevent silent data corruption or tampering during computation.
- Memory integrity tree: A Merkle tree structure verifies that data read from encrypted memory has not been modified, replayed, or reordered by a physical attacker
- Replay protection: Hardware counters prevent an attacker from substituting current data with a previously valid encrypted memory state
- Example: Intel SGX2 and TDX implement integrity protection that detects bus-level snooping and cold-boot attacks on memory contents
Code Confidentiality
TEEs protect not only the data being processed but also the proprietary algorithms and models operating on that data.
- Encrypted code loading: Application binaries are decrypted only inside the enclave, preventing reverse engineering by the host
- IP protection for ML models: Neural network weights and architecture remain opaque to infrastructure operators
- Example: Financial institutions use Confidential Computing to run proprietary trading algorithms on sensitive client data without exposing either the model or the data to the cloud provider
Confidential Computing vs. Other Encryption States
How hardware-based TEEs compare to other encryption methods across the three states of data: at rest, in transit, and in use.
| Feature | Confidential Computing | Encryption at Rest | Encryption in Transit |
|---|---|---|---|
Data state protected | Data in use (memory/CPU) | Data at rest (storage) | Data in transit (network) |
Protection mechanism | Hardware-based TEE (e.g., Intel SGX, AMD SEV) | Disk/volume encryption (e.g., AES-256) | Transport layer encryption (e.g., TLS 1.3) |
Protects against host OS compromise | |||
Protects against hypervisor compromise | |||
Protects against cloud provider access | |||
Protects against physical disk theft | |||
Protects against network interception | |||
Hardware root of trust required |
Frequently Asked Questions
Clear, technically precise answers to the most common questions about hardware-based Trusted Execution Environments and their role in protecting data in use.
Confidential computing is a hardware-based security paradigm that protects data in use by performing computation within a hardware-based Trusted Execution Environment (TEE). Unlike traditional encryption that protects data at rest (storage) and in transit (network), confidential computing isolates sensitive workloads from the host operating system, hypervisor, and even cloud providers. The TEE—implemented via technologies like Intel SGX, AMD SEV-SNP, or Arm CCA—creates a secure enclave in the CPU where code and data are decrypted only inside the processor. This hardware-enforced isolation ensures that even a compromised kernel or a malicious insider with physical access cannot exfiltrate plaintext data. The process involves:
- Attestation: A cryptographic mechanism where the TEE generates a signed report proving its identity and that the correct code is loaded, verifiable by a remote party before any secrets are released.
- Memory encryption: The CPU automatically encrypts enclave memory pages, preventing DRAM snooping or cold-boot attacks.
- Sealing: Data can be encrypted to a specific enclave's identity, ensuring it can only be decrypted by that exact code on that exact CPU.
Enabling Efficiency, Speed & Accuracy
Intelligent Analysis, Decision & Execution
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Talk to Us
Search across company data
Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.
Useful when people spend too long searching or get different answers from different systems.

Automate internal workflows
Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.
Useful when repetitive work moves across multiple tools and teams.

Add AI to products and internal tools
Build assistants, guided actions, or decision support into the software your team or customers already use.
Useful when AI needs to be part of the product, not a separate tool.
Related Terms
Confidential computing relies on a constellation of complementary technologies and standards. These related concepts form the foundation for hardware-enforced data-in-use protection.
Memory Encryption
Hardware-enforced encryption of data while it resides in system DRAM, preventing physical attackers from extracting plaintext via cold-boot attacks, bus snooping, or DIMM interposers. Key technologies include:
- AMD SME (Secure Memory Encryption): Encrypts entire system memory with a single key
- Intel TME (Total Memory Encryption): Transparently encrypts all data leaving the processor package
- AMD SEV-ES: Encrypts CPU register state on each VM exit
Memory encryption is the last line of defense against physical hardware attacks, ensuring that even a compromised hypervisor or stolen DIMM yields only ciphertext.
Secure Enclave
A hardware-isolated region of memory and execution within a TEE that is inaccessible to any process outside it, including privileged system software. Enclaves operate with:
- Encrypted memory pages that are decrypted only inside the CPU package
- Hardware-enforced isolation from the OS, hypervisor, and DMA devices
- Sealed storage for persisting encrypted data to untrusted storage
Enclaves are the execution container for confidential workloads. Code and data are loaded into the enclave, verified via attestation, and then executed in complete isolation from the rest of the system.
Homomorphic Encryption
A cryptographic scheme that allows computation directly on encrypted data without ever decrypting it. Unlike TEEs, which decrypt data inside a secure hardware boundary, homomorphic encryption keeps data encrypted throughout the entire computation lifecycle.
Key distinctions from confidential computing:
- No hardware trust dependency: Security is purely mathematical
- Extreme computational overhead: Operations are 1,000-1,000,000x slower than plaintext
- Complementary use case: Often combined with TEEs for multi-party scenarios
Fully Homomorphic Encryption (FHE) remains an active research area, with practical deployment limited to narrow, low-latency-tolerant applications.

About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
Partnered with leading AI, data, and software stack.
How We Work
Custom AI workflows for your Business
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
01
Review the use case
We understand the task, the users, and where AI can actually help.
Read more02
Pick the right approach
We define what needs search, automation, or product integration.
Read more03
Build the first useful version
We implement the part that proves the value first.
Read more04
Improve from there
We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
Talk to Us