All analysis was generated autonomously, without human review. Scores are analytical opinions drawn from the cited public sources, without hands-on testing. They are not audits, certifications, investment reports, purchasing advice, or evaluations of quality.
Confident Security sells CONFSEC, a service that runs AI models on encrypted prompts so the provider and operator cannot read them, copying the approach Apple built for its own devices. The company is betting that privacy becomes a requirement for AI in healthcare, finance, and government before the cloud platforms and model providers build that privacy in themselves. To get there first, it published its core as an open standard, OpenPCC, an openly licensed specification with a source-available server, so others can adopt it rather than a rival's. Founder Jonathan Mortensen sold two prior companies, and backers include Decibel and Halcyon. But no customer is named yet, only talks with banks and browsers, so the next test is a named deployment a customer will stand behind.
| Description | Confident Security offers CONFSEC, an inference API that runs AI models on encrypted prompts and outputs so the model provider and operator cannot read the data, built on its open-source OpenPCC standard. | [f1] |
|---|---|---|
| Founded | 2024 | [f2] |
| HQ | San Francisco, California, US | [f2] |
| Latest funding | Seed, $5M (2025) | [f3] |
| Product | What it does |
|---|---|
| CONFSEC | Verifiably-private inference API that keeps prompts, outputs, and logs encrypted and hardware-attested so the operator cannot read them, based on the OpenPCC standard. |
AI Defense Matrix
| Govern | Identify | Protect | Detect | Respond | Recover | |
|---|---|---|---|---|---|---|
| AI-Workload Platforms Inference servers, training platforms, vector DB platforms, and the model-loading supply chain. | ||||||
| AI Orchestration Tools Agentic orchestration tools, plus their plugins, skills, hooks, system prompts, scaffolding, harnesses, configuration settings, and MCP clients on user devices. | ||||||
| AI-Generated Code Code produced by AI tools, AI-assisted reviews, AI-generated infrastructure-as-code and tests, and vibe-coded apps that bypass CI/CD. | ||||||
| AI Gateways & Routers MCP proxies and gateways, LLM routers, outbound AI-service traffic, shadow AI egress, and model-registry traffic. | ||||||
| AI Model Model weights, fine-tuning checkpoints, model cards, registries, AIBOM, and the third-party LLMs your enterprise consumes. | ||||||
| Training Data Datasets used for training, fine-tuning, and continued learning. | ||||||
| Runtime AI Data User prompts, inference inputs, RAG content, vector DB content, persistent agent memory, and interaction history. | ||||||
| AI Agent Identities AI agents as non-human principals, plus credentials, keys, permission scopes, service accounts, and delegation chains across agents and tools. |
CONFSEC is a verifiably-private inference API that keeps prompts, outputs, and logs confidential through encryption and hardware attestation, routing requests through Oblivious HTTP so the operator cannot read them. These capabilities are mapped to the AI Defense Matrix. [f4]
How well the company can compete in its security market, scored across eight dimensions against public evidence.
| Dimension | Score |
|---|---|
| Problem Clarity How precisely the company defines its problem, with evidence the problem exists at the scale claimed. | 3/5 |
| Capability Depth How specific the technical capabilities are, with evidence beyond marketing claims such as docs, demos, and third-party validation. | 4/5 |
| Market Timing Whether the market is ready for this product, with evidence that buyers are actively seeking solutions. | 3/5 |
| Team Credibility Demonstrated domain expertise with public signals such as prior exits, publications, and industry recognition. | 4/5 |
| GTM Proof Evidence of actual traction (customers, revenue signals, partnerships) beyond stated intentions. | 2/5 |
| Funding Efficiency Whether funding matches go-to-market ambition, with signs of capital-efficient growth. | 3/5 |
| Category Clarity Whether the company creates or fits a recognizable category that buyers can quickly place in their stack. | 3/5 |
| Incumbent Defensibility How vulnerable the core value proposition is to absorption as a feature by a platform vendor. | 2/5 |
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A closer look at the company's product strategy, measuring how defensible it is against market forces and examining the eight areas behind it.
reinforce or reposition
| Dimension | Score |
|---|---|
| Value Delivery Does the product sell software as the product, or judgment, trust, or accountability with software as the delivery mechanism. | 1/3 |
| Switching Cost How expensive leaving is for a customer: data portability, integrations, learned workflows, network effects, regulatory data residency. | 2/3 |
| Compliance Moat Whether certifications, liability acceptance, or audit trails block an easy replacement. | 1/3 |
| Problem Complexity Whether the product requires ML, optimization, real-time systems, or years of specialized expertise. | 3/3 |
| Buyer Profile Whether buyers are SMB operators, mid-market IT teams, or regulated enterprises and governments with procurement gates. | 2/3 |
| Layer Whether the product is an end-user application, a platform with application features, or infrastructure other applications depend on. | 3/3 |
| Proprietary Data, Content, or IP Whether the product accumulates datasets, content licenses, or IP that a rival cannot recreate from scratch. | 1/3 |
Unlock the Full Analysis
The reasoning for the scores, the strategy deep dive, the business risks, and more. AI access comes with the purchase, so your AI tools can read the full profile too. You keep 12 months of access.
One-time purchase: $20 per profile.
UnlockReading several? Unlock the entire catalog.
| Id | Source | Tier | Accessed |
|---|---|---|---|
| f1 | Confident Security homepage (verifiably-private inference API) | official | 2026-07-03 |
| f2 | TechCrunch on Confident Security stealth launch (year-old company) | press | 2026-07-03 |
| f3 | The AI Journal on Confident Security OpenPCC launch (seed funding and team) | press | 2026-07-03 |
| f4 | CONFSEC (AI Defense Matrix Catalog mapping) | other | 2026-07-03 |
| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | Confident Security homepage (verifiably-private inference API) “Verifiably-private Inference API. Develop AI products without worrying about security, privacy, or compliance. We take financial responsibility for any breach or misuse. Based on OpenPCC, the open-source standard that ensures all AI interactions are verifiably-private.” | official | 2026-07-03 |
| s2 | TechCrunch on Confident Security stealth launch (traction, founding, mechanism) “It's still early days for the year-old company, but Mortensen said CONFSEC has been tested, externally audited, and is production-ready. The team is in talks with banks, browsers, and search engines, among other potential clients, to add CONFSEC to their infrastructure stacks.” | press | 2026-07-03 |
| s3 | The AI Journal on the OpenPCC launch (seed funding, founder, team) “Confident Security raised $5 million in seed funding from Decibel, Ex/Ante, South Park Commons, Halcyon, and SAIF. Mortensen is a two-time founder with prior exits to BlueVoyant and Databricks. The team's background spans Google, Apple, Databricks, Red Hat, and HashiCorp.” | press | 2026-07-03 |
| s4 | Pulse 2.0 on Confident Security's stealth raise (investors) “launched with $4.2 million in funding from Decibel, South Park Commons, Ex Ante, and Swyx. ... The team, led by two-time founder Jonathan Mortensen, comprises experts ... including Google, Apple, and Johns Hopkins.” | press | 2026-07-03 |
| s5 | OpenPCC open-source repository (open, auditable framework) “OpenPCC is an open-source framework for provably private AI inference, inspired by Apple's Private Cloud Compute, fully open, auditable, and deployable on your own infrastructure. It enforces privacy with encrypted streaming, hardware attestation, and unlinkable requests.” | official | 2026-07-03 |
| s6 | Confident Security pricing (per-token, 2x market rate) “Beyond compliant AI for just 2x market rate. Mistral 7B $0.25 per 1M input tokens, $0.25 per 1M output tokens. Llama 4 Scout 16x17B $0.36 per 1M input tokens, $1.18 per 1M output tokens. Last updated July 9th, 2025.” | official | 2026-07-03 |
| s7 | Confident Security compliance page (probed trust surface, guarantees framing) “Why pay for promises when you can have absolute guarantees? Your data are guaranteed to never be used in training AI models, never be shared with a third party. ... HIPAA Compliant AI. PCI DSS Compliant AI. GDPR Compliant AI. SOC 2 Type 2 Compliant AI.” | official | 2026-07-03 |
| s8 | CONFSEC in the AI Defense Matrix Catalog (matrix coverage) “Keeps prompts, outputs, and logs confidential through encryption and attestation, so prompts are never logged, retained, used for training, or sent to third parties. Routing requests through Oblivious HTTP so operators cannot link or read individual requests.” | other | 2026-07-03 |
| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | Confident Security homepage (verifiably-private inference API) “Verifiably-private Inference API. Develop AI products without worrying about security, privacy, or compliance. We take financial responsibility for any breach or misuse. Based on OpenPCC, the open-source standard that ensures all AI interactions are verifiably-private.” | official | 2026-07-03 |
| s2 | TechCrunch on Confident Security stealth launch (traction, mechanism, founding) “It's still early days for the year-old company, but Mortensen said CONFSEC has been tested, externally audited, and is production-ready. The team is in talks with banks, browsers, and search engines, among other potential clients, to add CONFSEC to their infrastructure stacks.” | press | 2026-07-03 |
| s3 | The AI Journal on the OpenPCC launch (funding, founder, team, components) “Confident Security raised $5 million in seed funding from Decibel, Ex/Ante, South Park Commons, Halcyon, and SAIF. Mortensen is a two-time founder with prior exits to BlueVoyant and Databricks. The team's background spans Google, Apple, Databricks, Red Hat, and HashiCorp.” | press | 2026-07-03 |
| s4 | Pulse 2.0 on Confident Security's stealth raise (investors, founder, team) “launched with $4.2 million in funding from Decibel, South Park Commons, Ex Ante, and Swyx. ... The team, led by two-time founder Jonathan Mortensen, comprises experts ... including Google, Apple, and Johns Hopkins.” | press | 2026-07-03 |
| s5 | OpenPCC open-source repository (open, auditable framework) “OpenPCC is an open-source framework for provably private AI inference, inspired by Apple's Private Cloud Compute, fully open, auditable, and deployable on your own infrastructure. It enforces privacy with encrypted streaming, hardware attestation, and unlinkable requests.” | official | 2026-07-03 |
| s6 | Confident Security pricing (per-token, 2x market rate) “Beyond compliant AI for just 2x market rate. Mistral 7B $0.25 per 1M input tokens, $0.25 per 1M output tokens. Llama 4 Scout 16x17B $0.36 per 1M input tokens, $1.18 per 1M output tokens. Last updated July 9th, 2025.” | official | 2026-07-03 |
| s7 | Confident Security compliance page (probed trust surface, guarantees framing) “Why pay for promises when you can have absolute guarantees? Your data are guaranteed to never be used in training AI models, never be shared with a third party. ... HIPAA Compliant AI. PCI DSS Compliant AI. GDPR Compliant AI. SOC 2 Type 2 Compliant AI.” | official | 2026-07-03 |
| s8 | CONFSEC in the AI Defense Matrix Catalog (matrix coverage) “Keeps prompts, outputs, and logs confidential through encryption and attestation, so prompts are never logged, retained, used for training, or sent to third parties. Routing requests through Oblivious HTTP so operators cannot link or read individual requests.” | other | 2026-07-03 |
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