01Alert investigation
Turn scattered signals into one incident story.
Correlate identity, permission, posture, and activity context near the customer environment, then return a ranked explanation to the security team.
Scenario: distributed inference for cyber AI productsSee the runtime
Cyber AI infrastructure
Put security intelligence closer to every customer.
Run your security models near the identities, events, and policies they need. Investigate faster, keep sensitive context inside approved boundaries, and tune behavior for every tenant.
Built for SaaS security, identity risk, posture, data exposure, and agent security products.
CUSTOMER ENVIRONMENTAPPROVED INFERENCE ZONE
TENANT SLMPROFILE 07 · LOCAL CONTEXT
IDIDENTITIES
GSAAS GRAPH
AACTIVITY
PPOLICY
APPROVED CAPACITY
NYR RUNTIME
SECURITY PRODUCTPRIORITIZED FINDING
IDENTITY · GRAPH · ACTIVITY · POLICY STAY IN THE APPROVED ZONE
01Faster investigationShorter path to live context
02Local data boundariesProcess sensitive evidence in bounds
03Tenant-specific reasoningFit each customer’s risk model
04Resilient responseUse approved distributed capacity
Where local context changes the answer
Make every security decision
tenant aware.
Cyber AI is only as useful as the context behind it. NYR gives models a local path to the customer-specific signals that turn a generic alert into a decision a team can trust.
02Identity risk
Prioritize access risk with tenant context.
Evaluate privilege, role, ownership, and behavior against the customer’s real operating model instead of a generic global baseline.
03Posture drift
Explain why a configuration change matters.
Reason over local policy, application settings, and sensitive data exposure so teams see the impact, not only the misconfiguration.
04Agent security
Watch every agent’s identity, reach, and behavior.
Inspect non-human identities, connected applications, permission paths, and anomalous actions while keeping high-sensitivity context in bounds.
HAVE A LIVE USE CASE?Bring one investigation path.
Map the inference path We’ll map the model, evidence sources, customer boundary, and response target.
The cyber inference path
Keep the product global.
Place inference where the evidence lives.
NYR operates beneath your security product. Your team keeps the model and experience; NYR handles placement, tenant profiles, health, and the path to approved local context.
IDENTITYPOSTUREEVENTSPOLICY
APPROVED CAPACITYNYR RUNTIMEtenant model · local profile
PRODUCT OUTPUTFINDINGranked · explained · actionable
Connect approved context
Bring identity, SaaS graph, posture, policy, and event signals into the customer’s approved inference zone.
Place the right model
NYR schedules your signed SLM or LLM on approved capacity close to the environment it serves.
Tune by tenant
Apply the customer’s vocabulary, risk thresholds, integrations, controls, and response rules at runtime.
Return a useful decision
Send the product a prioritized finding, explanation, and recommended next action without moving the full context set.
Why distributed cyber AI
Move the model closer.
Move less sensitive context.
CENTRALIZED INFERENCE
The customer waits while context travels.
- PATHEvidence crosses a longer network route
- DATAMore sensitive context leaves its source boundary
- FITGlobal defaults miss tenant-specific meaning
- RESILIENCEOne remote serving path carries the experience
Generic context. More movement. A slower path to action.
WITH NYRDISTRIBUTED PATH
Inference meets the evidence in the approved zone.
- PATHModels run closer to identities, events, and policy
- DATAHigh-sensitivity context can stay in bounds
- FITEach tenant gets its own model and operating profile
- RESILIENCEHealth-aware placement uses approved capacity
Faster investigation. Better context. Clearer customer value.
One product, many tenant realities
Reuse the security product.
Personalize the decision layer.
A financial institution, hospital, and software company can share your core product without sharing the same language, risk threshold, data rules, or remediation playbook.
ONE ROLLOUT3 LOCAL PROFILES
Meet each customer where they operate
One runtime across
approved cyber environments.
Customer cloud
Place a tenant model inside the customer-controlled cloud boundary and connect to approved services locally.
Regional zone
Serve multiple nearby environments from approved regional capacity with the right latency and data policy.
Local infrastructure
Run where high-sensitivity evidence or disconnected operations require a local serving path.
START WITH ONE CUSTOMERChoose the first approved environment.
Plan the first rollout Customer cloud, regional zone, or local infrastructure. We’ll map the right first path.
Cyber AI infrastructure FAQ
What product teams ask before a distributed rollout.
01Why run cyber AI models closer to each customer environment?
Cyber investigations depend on fresh, high-context signals. Placing inference closer reduces the network path to those signals, supports local processing requirements, and gives the security product a more reliable serving path.
02Does NYR replace the security platform or its detection engine?
No. NYR is the distributed inference and orchestration layer underneath the product. The cyber vendor keeps its models, product experience, detections, and customer relationship.
03Can every tenant use a different model or policy profile?
Yes. A shared rollout pattern can select different model versions, prompts, vocabulary, thresholds, integrations, and data rules for each approved customer zone.
04Where can the cyber workload run?
NYR is designed to place workloads across approved distributed capacity, including customer-controlled cloud environments, regional zones, and local infrastructure, with health-aware placement and failover policies.
Bring a real cyber workflow
Where should your security model meet the evidence?
Bring the model, customer environments, context sources, data boundaries, and response target. We’ll map the right placement and tenant profile for the first rollout.
Map my cyber deployment