Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsFortanix’s approach is to protect AI search while it is being performed, not just encrypt stored records. Confidential computing can isolate prompts and retrieved data in a protected runtime, with access controlled by cryptographic keys. Fortanix announced a product called Confidential Data Search in 2023; a 2024 report described the company’s work on private AI search, while Fortanix’s March 2026 materials focus on a broader Confidential AI platform. The available information does not establish that Confidential Data Search is now a generally available standalone product.
How private AI search is meant to work
An AI search system typically takes a human or machine query, finds relevant material in a data source, and supplies that material to a model so it can generate an answer. Sources may include conventional databases, knowledge graphs, or vector databases, which organize records by encoded meaning rather than relying only on exact keyword matches.
Encryption at rest protects stored records, and encryption in transit protects data moving between systems. But a service must generally process usable data to search it. Confidential computing addresses that exposure by running work inside a hardware-backed protected environment, often called a trusted execution environment. In the Fortanix description reported by Dark Reading on April 2, 2024, authorized access to the protected environment is governed by keys, and data is processed within it.
- A user or service submits a query. The query can reveal intent or sensitive context even when the underlying records are encrypted at rest.
- The search runtime accesses relevant records. A vector search may compare a query with embeddings, numeric representations of data or meaning. The search process must protect both the query and the material used to find a match.
- Retrieved content is supplied to an AI model. The model uses that context to produce an answer, so the prompt, retrieved information, model and output all matter to the security boundary.
- Access is conditioned on trust checks. Attestation can verify that a runtime has an expected configuration before cryptographic keys are released. This is intended to prevent an unverified or tampered environment from receiving access.
The goal is not merely to conceal a database. It is to limit exposure of the search initiator’s intent, protect the confidentiality and integrity of retrieved information and embeddings, and maintain control while data is being used by the search and inference components.
Free tools Windows power users keep installed
One-click scans. No signup required.
#1 Best Overall
- Compact and Efficient Design: The FortiGate 40F is designed for small to mid-sized businesses and enterprise branch offices, featuring a compact, fanless desktop form factor that ensures quiet operation and minimizes space usage.
- Robust Connectivity Options: Equipped with 5 GE RJ45 ports, including 1 WAN port and 4 internal ports, this model provides essential connectivity and flexibility for various network configurations in a small-scale environment.
- High-Performance Security: Offers up to 1 Gbps IPS throughput and 600 Mbps threat protection throughput, using Fortinet’s purpose-built security processor technology to deliver industry-leading performance and protection for SSL encrypted traffic.
- Advanced Threat Protection: Integrated with Fortinet’s AI-powered FortiGuard Labs, the FortiGate 40F offers comprehensive cybersecurity, identifying and mitigating both known and unknown threats to maintain robust security across your network.
- Simplified Management and Deployment: Features a user-friendly management console that provides comprehensive network automation and visibility, coupled with Zero Touch Integration with Fortinet’s Security Fabric for easy deployment.
What Fortanix says its approach protects
Dark Reading’s 2024 account described Fortanix’s objective as securing both the search prompt and the retrieval layer used by large language models. That includes protecting the person’s or system’s query and the vector embeddings or other information used to retrieve relevant records. It also reflects concerns beyond secrecy: organizations may need to ensure retrieved content has not been altered and that data is accessed with appropriate permission.
In its March 18, 2026 Confidential AI announcement, Fortanix described a wider set of protections: proprietary model weights remain encrypted, prompts and outputs are encrypted in memory, keys are released only to verified runtimes, and deployment environments are checked for tampering. The announcement names NVIDIA Confidential Computing alongside Fortanix Confidential Computing Manager and Data Security Manager as parts of the deployment. These are statements about the announced platform direction; they do not, by themselves, document the configuration or protections of every deployment.
Rank #2
- HARDWARE PLUS SECURITY SERVICES: FortiGate-60F Firewall Appliance bundled with 1 year of FortiCare Premium and FortiGuard Unified Threat Protection.
- UNIFIED THREAT PROTECTION (UTP): Secures against advanced online threats with comprehensive web filtering and anti-botnet technologies.
- OPTIMIZED FOR MEDIUM-SIZED BUSINESSES: Tailored for businesses needing robust security without the infrastructure of larger enterprises.
- RELIABLE CUSTOMER SUPPORT: FortiCare Premium ensures high-quality support and service continuity.
- EFFECTIVE PROTECTION: Employs advanced filtering technologies to safeguard against sophisticated threats.
- Data in use: Confidential computing is intended to protect data while it is processed, addressing a gap left by encryption only at rest and in transit.
- Prompt and output privacy: Fortanix’s 2026 announcement says these remain encrypted in memory. The release does not specify every supported model, runtime, or deployment configuration.
- Model confidentiality: Fortanix says proprietary model weights stay encrypted, which matters where model assets themselves are sensitive.
- Integrity and access control: Runtime verification and conditional key release aim to restrict access to trusted environments and detect tampering.
Why vector-database search raises a privacy issue
Vector databases can represent structured or unstructured records as embeddings, then retrieve items based on semantic similarity. This can make it possible to find relevant information even when a query does not use the same words as the source material. It also creates more assets to protect: the query embedding, stored embeddings, matched records, and the process that ranks and returns them.
Fortanix’s stated aim is to protect the privacy of the search initiator as well as the confidentiality and integrity of embeddings. That distinction matters. A system could encrypt the original files yet expose the query, similarity results, or intermediate data to infrastructure operators or other components outside its protected boundary. Whether a particular implementation actually shields each of those elements depends on its design and integrations; the available announcements do not provide a complete technical specification for every vector database or model stack.
Rank #3
- 【Up to 1100 Mbps VPN Speed 】 Hardware-accelerated WireGuard and OpenVPN-DCO deliver up to 1100 Mbps VPN throughput, over 3× faster than Brume 2 for smooth remote access and file transfers.
- 【Three 2.5G Ports & Multi-WAN】Tri-port 2.5GbE design with flexible WAN LAN configuration supports multi-gigabit wired setups, dual-ISP Multi-WAN and failover to keep home and SOHO networks online.
- 【Stealth VPN Obfuscation】VPN obfuscation disguises VPN traffic as regular HTTPS, helping you evade blocking, bypass restrictive networks and maintain stable, private connections.
- 【DPI protection】Deep Packet Inspection with visual dashboards blocks adult/gambling/malicious sites, while SQM and QoS prioritize gaming, calls, and video when bandwidth is tight
- 【OpenWrt & USB 3.0 Expansion】OpenWrt with 1GB DDR4 and 8GB eMMC lets you install plugins and build VPN, ad-blocking or NAS, while USB 3.0 Type‑C connects high-speed storage or 4G/5G dongles
Who might benefit
The strongest fit is an organization that wants AI search over sensitive records but must control who can see queries and retrieved content, where data is processed, and what conditions permit access. Dark Reading highlighted healthcare, banking, and government as settings where privacy, consent, and permissioning are central concerns. These sectors may also have obligations around residency and regulatory controls, but a confidential-computing product does not by itself establish compliance with any particular law or policy.
- Healthcare: Search may involve sensitive clinical or administrative records, so access boundaries and permitted use of patient information are important.
- Banking: Financial records and internal knowledge can be sensitive, making protected processing and auditable access controls relevant.
- Government: Data location, sovereignty, and deployment control may influence whether cloud, on-premises, or another environment is acceptable.
What is known about the product timeline
Fortanix announced Confidential Data Search on June 26, 2023, describing it as a way to perform high-performance searches across encrypted databases without compromising security or privacy regulations. At that time, the company said the solution was in private preview and targeted general availability for the second half of 2023. That target is historical, not confirmation that general availability occurred.
Rank #4
- Runs UniFi Network for full-stack network management
- Manages 30+ UniFi Network devices and 300+ clients
- 1 Gbps routing with IDS/IPS
- Multi-WAN load balancing
- 0.96" LCM status display
The 2024 Dark Reading story characterized Fortanix as building a security layer for AI search and reported partner discussions with customers. It did not present an independent performance study or a consumer-facing product listing. Fortanix’s March 2026 announcement instead presents Confidential AI as part of a broader enterprise data and AI security direction, alongside Confidential Computing Manager and Data Security Manager. Current platform positioning therefore should not be read as proof that the original Confidential Data Search announcement remains a separately available product under the same terms.
How to assess a confidential AI search deployment
Organizations evaluating this approach should verify the details of the actual deployment rather than relying on the general promise of confidential computing. The meaningful questions are about the full path from query to generated answer:
Recommended Free Tools
- In-use protection: Which components run inside the protected environment: embedding, vector search, retrieval, model inference, or only selected steps?
- Attestation and keys: What is checked before keys are released, who controls the policy, and what happens if an environment fails verification?
- Encryption coverage: Are prompts, outputs, embeddings, retrieved records, and model weights encrypted while in memory, and are there exceptions such as logs or debugging paths?
- Data location: Where are records processed, and can the deployment meet the organization’s geographic and sovereignty requirements?
- Integration and operations: Which vector databases, model runtimes, cloud services, and on-premises environments are supported, and how are updates, monitoring, backups, and incident response handled?
- Threat boundaries: Which actors and failure modes does the design address, and which remain outside the protected boundary? Confidential computing does not automatically secure application logic, user authorization, model behavior, or every surrounding service.
How to read Fortanix’s performance claim
Fortanix’s June 2023 announcement described Confidential Data Search as “thousands of times faster than current technologies.” The cited material does not include a test protocol, comparison baseline, workload, or independent benchmark, so this should be treated as a vendor claim rather than a verified performance result. Performance for a real deployment will depend on the workload, data, hardware, security configuration, and integrations.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
