AI-Native Autonomous SecOps Engine
Autonomous SOC, at machine speed.
The road to Autonomous SOC is not paved with more data or bigger models, but with the intelligence to find the right evidence, the judgment to understand it, and the autonomy to act on it.
ApexAI ingests your alerts, scores every case on a 0–100 risk index, investigates, links related cases into attack stories and proposes the response. It carries the alert volume so your analysts work the cases that matter.
Nothing that changes a real system — block, isolate, revoke, rotate — runs without a person’s approval, under a decision policy signed with ML-DSA-65. The most critical cases need two approvers.
Case [ID] · host [workstation-name]
- Identity checked
- Host and network checked
- Linked to 2 related cases
- Likely next ATT&CK step noted
Decision policy v[n] · signed ML-DSA-65
On-premise first
Ships for your data centre or private cloud, not as SaaS.
Local LLM by default
Reasoning runs on an Ollama-compatible model you host. Cloud LLMs are opt-in.
Post-quantum signed
ML-DSA-65 decision policies, ML-DSA-87 licences, ML-KEM-1024 key exchange.
Human approval gate
A fixed design rule, with a two-person rule and a kill switch.
Why now
Security operations were built for human-paced attacks. That no longer holds.
Attackers automate. Most SOC tooling, and most "AI SOC" add-ons, still assume a person reads every alert.
01
Attacks run at machine speed
Automated and AI-assisted intrusions move from first access to impact in minutes, and exploitation follows disclosure faster every year. Human-paced triage cannot keep up.
02
SIEM and SOAR operations are outdated
Rule-centric SIEMs flood teams with alerts, static playbooks break when the environment changes, and rules go blind when log fields drift.
03
Teams are stretched
A lasting talent shortage, analyst burnout and the cost of 24/7 coverage. Managed providers still scale by adding people.
04
Frontier add-ons do not close the loop
Large cloud models are token-hungry at SOC volume, add latency and send sensitive logs off-premises. Most summarise; few detect, verify, act and learn.
05
Agentic AI is a stepping stone
Chaining a frontier model through tool calls multiplies token and infrastructure cost, is hard to audit, and starts mostly from scratch on every run.
06
No local option
Many AI offerings cannot run on your own infrastructure or on air-gapped networks, which shuts out banking, government, telecom and energy.
Read the full story: the machine-speed threat era Beyond agentic: why SOC autonomy must get cheaper as it learns
One platform
One AI-native SecOps platform: three fronts, one core.
API and data protection, post-quantum cryptography and the autonomous SOC on one platform and one source of truth, instead of a stack of point products.
In development
AI-native API Gateway
API, AI and MCP traffic through one governed door: API security, prompt protection and protection for AI agents and tools, on a high-performance Rust gateway.
In development
Data Gate
Next-generation data security, not a database firewall: protects data in use, field by field, as it moves through applications and APIs.
Signing available
Post-quantum cryptography
Licences, audit records and decision policies are signed with NIST post-quantum algorithms today; post-quantum transport is on the roadmap.
SecOps core: Autonomous SOC and remediation
Detect, investigate and verify at machine speed. Remediate through proposals people approve, on the same path for gateway actions and SIEM/EDR actions.
- Intelligence to find the right evidence
- Judgment to understand it
- Autonomy to act on it, proposal-first and human-approved
Integrates with your stack
Keep the SIEM and EDR you have. No rip and replace.
ApexAI reads your data where it lives, with no mass copying of logs, turns your existing SIEM and XDR into an autonomous SOC, and sends responses back through your own tools as proposals people approve.
- Elastic Read-only
- CrowdStrike Falcon Read-only
- Palo Alto Cortex XSIAM Read-only
- Microsoft Sentinel Planned
- Threat intelligence feeds (STIX/TAXII) Read-only
Vendor names are trademarks of their owners. No endorsement is implied.
See every integration and its statusThe autonomous loop
From alert to approved action, and back into learning.
Every alert goes through the same five steps. The AI does the volume work; a person decides wherever an action would touch a real system.
- 01
Ingest
Alerts arrive, signed and deduplicated.
- 02
Score
Every case gets a 0–100 risk index.
- 03
Investigate
Evidence checks, written down.
- 04
Propose
Actions chosen under signed policy.
- 05
Approve & learn
People decide; outcomes teach.
Risk decides who works a case
Four tiers, from proven noise to critical.
The tier follows from the case’s risk index. People are always in the loop from tier 2 up.
0
Noise
AI, with sampled QA
Closed only below a conformal threshold calibrated on your verdicts, and only for detections people judged benign before.
1
Investigate
AI
Runs evidence checks, writes down what it checked, links the case to related ones.
2
Act
A person approves
Containment is proposed with the risk index and the AI’s reasoning.
3
Critical
Two people + Coordinator AI
An incident is opened; any action needs two approvers.
Platform
Six modules on one Core. License the platform or a single module.
One identity, one console, one licence. Each module also runs and is licensed on its own.
BlueTeam AI
Autonomous SOC, threat intel, UEBA, knowledge graph
Ingests SIEM alerts, scores risk, triages, investigates, links related cases into attack stories and closes proven noise.
Coordinator AI
Case coordination
Takes escalated incidents, reasons over them on a local LLM and runs multi-step response workflows.
Remediate AI
Remediation and AI governance
Chooses actions under signed, versioned decision policies, with playbooks and an action engine behind the approval gate.
PQC Shield
PQC / CBOM scanning and PQC Gate
Finds quantum-vulnerable cryptography in your code, tracks its remediation and gives migration designs to ML-KEM and ML-DSA.
Privacy Shield
Data Gate, Data Platform and the privacy engine
Protects sensitive fields before they reach ETL, analytics or APIs. Computation can run on FHE ciphertexts, so the server never sees the plaintext.
Govern
Access, compliance and risk governance
Access certification, just-in-time access, autonomous compliance, vendor risk, breach response, PIA and a sovereignty map.
Security & Trust
Built to be trusted with your environment.
Honest by construction
An action with no connected backend says “not applied” instead of reporting success. Nothing in an API response is invented.
Every decision recorded
Each AI decision can be reviewed and is counted against people’s verdicts, so accuracy is measured, not asserted.
Your data stays yours
Field-level protection — FHE, AES-256-GCM, blind index, hybrid ML-KEM — before data reaches ETL, analytics or APIs.
Results
What pilots measure.
These are placeholders. ApexAI publishes a figure only once it is measured on a customer’s own cases and the customer approves it.
[X]%
of alerts closed as proven noise under the calibrated threshold
[X] min
median time from alert to a proposal with evidence
[X]%
agreement between AI verdicts and analysts’ verdicts
- [Customer logo]
- [Customer logo]
- [Customer logo]
- [Customer logo]
See ApexAI on your own alerts.
A walkthrough with your team, in observe mode first: ApexAI scores and proposes, nothing acts on its own.