The finished product
AegisAI beside SentinelOne Singularity.
SentinelOne is among the strongest endpoint platforms on the market. The first two rows are mostly equivalent: both detect on the device, both keep protecting offline, and both roll ransomware damage back from shadow copies. On every other row AegisAI is ahead: where an incident is understood, who it is explained to, how the product learns, and what it costs to own.
Cloud-centred platform
- Analyst console — people investigate and decide
- Vendor platform, usually its cloud — data lake, correlation, threat hunting, generative assistant
- Endpoint agent — on-device detection and enforcement
Much of the understanding happens away from the machine.
AegisAI
- Plain-English incident, for the person at the machine
- Graduated response, rollback, signed forensic archive
- Triage Llama 3.2 3B + deep Llama 3.1 8B, fine-tuned by AegisAI and by you
- Rules, signatures, edge AI, sub-100 ms
- Signed, chained telemetry and WFP enforcement
Nothing leaves the box — and nothing needs to.
Today's leaders were built around a vendor platform. Their generative assistants help analysts investigate after the fact. The person at the affected machine gets an alert, not an explanation. Response is largely kill, quarantine or isolate, with shadow-copy rollback in the best of them. Their models are tuned centrally, on data pooled from all customers, so they learn the average customer rather than your machine. Their cost scales per endpoint, every year, to fund the pipeline behind them.
| SentinelOne Singularity | AegisAI — the finished product | |
|---|---|---|
| On-device detectionMostly Equivalent | Static and behavioural AI engines on the agent. Keeps protecting offline. | Sigma rules, YARA, signatures and four edge models on the device. Keeps protecting offline. |
| Ransomware recoveryMostly Equivalent | Rollback from Windows shadow copies, including registry keys and scheduled tasks. | Surgical rollback from shadow copies and a signed change journal, files and registry. |
| Incident explanationAegisAI is better: the explanation is written on the machine for its owner. SentinelOne’s assistant helps an analyst in the vendor console. | Purple AI: a generative assistant for analysts investigating in the SentinelOne console. | Every incident explained automatically, on the endpoint, by two fine-tuned local Llama models — for the person at the machine, with no console or analyst required. |
| Where the AI reasoning runsAegisAI is better: both models stay on the endpoint, so air-gap is the default rather than a separate edition. | Detection on the agent. Correlation, hunting and Purple AI on the SentinelOne platform — cloud by default, with on-premise editions for regulated and air-gapped sites. | All of it on the endpoint, both language models included. Air-gapped operation is the default, not an edition. |
| Detection contentAegisAI is better: the rules are open and auditable, and each author’s name travels with the verdict. | Proprietary engines and models. | Open, auditable content — SigmaHQ rules with their authors credited, YARA and ClamAV — beside the local models, plus the customer’s own rules and signatures. |
| ResponseAegisAI is better: it takes the least destructive step that fits, and a low-confidence verdict cannot destroy anything. | Kill, quarantine, remediate, network quarantine and rollback, per policy. | A four-rung ladder chosen by a confidence-and-severity router. Low confidence never destroys anything, and every action seals a signed evidence archive. |
| Evidence integrityAegisAI is better: the record is signed and hash-chained as it is collected, not only shielded from tampering afterwards. | Anti-tamper protection of the agent. | Telemetry Ed25519-signed and hash-chained at the source. Sealed, signed .aegis forensic archives. |
| LearningAegisAI is better: it adapts to this machine, not to the average customer, and the owner fine-tunes it on their own policy and incidents. | Models trained and updated centrally by the vendor. | Per-machine LoRA adapters from signed specialist feedback, shadow-evaluated against a frozen golden model — and fine-tuning by the owner, on the owner’s own policy and incidents. |
| Value and ownershipAegisAI is better: no cloud pipeline to fund. Updates sit beside what the customer owns and never overwrite its own rules or remove its own fine-tuning. | Per-endpoint annual subscription, in tiers. | A perpetual licence, no compulsory retainer; an optional retainer from USD 5 per machine per month for new rules, definitions, signatures and fine-tuned models. |
SentinelOne, Singularity and Purple AI are trademarks of SentinelOne, Inc. Its column reflects public information as of November 2026. The AegisAI column describes the finished product, not only this prototype.
What the market charges today
List or reported prices per endpoint, before the generative-AI layer where that is sold separately. For 100 endpoints over three years, SentinelOne Singularity Complete at list price comes to $53,997 and CrowdStrike Falcon Enterprise to $55,497, and that bill repeats for as long as the subscription runs. AegisAI’s optional retainer for the same 100 machines, at USD 5 per machine per month, comes to $18,000 over three years — and protection continues without it.
| Product | Price (list or reported) | Generative AI |
|---|---|---|
| SentinelOne Singularity | Complete $179.99, Commercial $229.99 per endpoint per year (list) | Purple AI, in “includes Purple AI” packages |
| CrowdStrike Falcon | Go $59.99, Pro $99.99, Enterprise $184.99 per device per year (reported list) | Charlotte AI, sold separately (reported per analyst seat or per action) |
| Microsoft Defender | Defender for Business $3 per user per month, up to five devices | Security Copilot: included compute with Microsoft 365 E5; otherwise $4 per provisioned compute unit per hour |
| Palo Alto Cortex XDR | Pro about $81–120 per endpoint per year (third-party estimates) | Cortex AgentiX, quote-based |
| Huntress Managed EDR | $8.99 per endpoint per month (50–99 endpoints), SOC included | — (human SOC) |
| AegisAI | Perpetual licence, no compulsory retainer. Optional retainer from USD 5 per machine per month for new rules, definitions, signatures and fine-tuning files, added beside the customer’s own | Both language models included, on the endpoint, trainable by the customer |
The market today: who comes closest
We surveyed the leading AI-driven endpoint platforms and the open, self-hosted alternatives in November 2026. Several run machine learning on the endpoint for detection. Several offer a generative assistant. A few can be self-hosted with a local language model on a server. None runs language-model reasoning on the protected machine itself, explains each incident to that machine’s owner, and lets the owner train the AI on their own policy. AegisAI is the only product designed to do all three.
- Yes
- Partly
- No, or not found in public material
| Product | Language-model reasoning on the protected machine | Explains each incident to the machine’s owner | Full capability with no network | Owner takes part in training the AI | Customer’s own detection rules | Open, auditable detection content |
|---|---|---|---|---|---|---|
| AegisAI (finished product) | Two Llama models, on the CPU | Every incident | Air-gapped by design | Model fine-tuning page, LoRA | Sigma, YARA, signatures | SigmaHQ, YARA, ClamAV |
| SentinelOne Singularity + Purple AI | Purple AI on the platform | Analyst console | Agent protects offline; on-prem editions since March 2026 | Vendor-trained | Custom rules | Proprietary |
| CrowdStrike Falcon + Charlotte AI | Cloud platform | Analyst console | Agent protects offline | Vendor-trained | Custom IOA rules | Proprietary |
| Microsoft Defender + Security Copilot | Microsoft cloud | Analyst portal | AV protects offline | Vendor-trained | Custom detections | Proprietary |
| Elastic Security (Elastic Defend + AI Assistant) | Self-hosted LLM on a server | Analyst in Kibana | With self-managed servers | Bring your own model; no tuning workflow | Custom rules | Open rule repository |
| Wazuh (open source) | Local LLM threat hunting on the server | Analyst dashboard | Self-hosted servers, not the endpoint itself | Bring your own model; no tuning workflow | Own rules; Sigma in 5.0 | Open source |
| Arctic Wolf Aurora (Cylance) | Classic ML, no language model | Managed SOC | ML built for limited connectivity | Vendor-trained | Policy settings | Proprietary |
| Norton (Gen Digital), consumer | NPU models for deepfake and scam checks only | Scam assistant, not incidents | Core AV offline | Vendor-trained | — | Proprietary |
Closest in concept, and where they stop
- SentinelOne pricing and platform packages: sentinelone.com/pricing, sentinelone.com/platform-packages
- CrowdStrike Falcon and Charlotte AI pricing guides: casrai.org/guides/crowdstrike-pricing; softwarecontractnegotiation.pages.dev/blog/crowdstrike-charlotte-ai
- Microsoft Defender for Business: microsoft.com/security/business/endpoint-security/microsoft-defender-business
- Security Copilot inclusion in Microsoft 365 E5: learn.microsoft.com/copilot/security/security-copilot-inclusion; samexpert.com/security-copilot-licensing-guide
- Cortex XDR estimates and Cortex AgentiX: costbench.com/software/cybersecurity/palo-alto-networks; investors.paloaltonetworks.com
- Huntress pricing: underdefense.com/blog/huntress-pricing-guide
- SentinelOne on-premise and air-gapped editions, 23 March 2026: businesswire.com/news/home/20260323813450
- Elastic, self-managed LLMs: elastic.co/docs/explore-analyze/ai-features/llm-guides/local-llms-overview
- Wazuh local-LLM threat hunting: securityinfowatch.com (Wazuh press release)
- Wazuh Sigma rules: documentation.wazuh.com/5.0-rc/user-manual/wazuh-indexer/wazuh-sigma-rules.html
- Arctic Wolf acquisition of Cylance: itpro.com, techzine.eu
- Norton on-device deepfake protection: newsroom.gendigital.com, gendigital.com