AI SOC for MSSPs and MDR Providers
Spharaka Sphere™ is a multi-tenant AI SOC platform built for managed security providers: every qualified alert investigated autonomously across every tenant, with real data separation, white label delivery and a deployment for each customer's rules.
What is an AI SOC for MSSPs?
An AI SOC for MSSPs is a security operations platform in which AI agents do the monitoring, triage, investigation and response across every tenant a provider serves. The provider's analysts set the policy, review the conclusions and take the decisions that need judgement. What separates it from an enterprise AI SOC is multi-tenancy: one console for the provider's team, and a separated estate for each customer behind it.
It is searched for under several names, and they describe the same product. An AI SOC platform for MSSPs, an AI SOC for managed security providers, an MSSP AI SOC platform, an autonomous SOC for MSSPs, an AI MDR platform, a multi-tenant SOC platform, a white label SOC platform, a SOC-as-a-Service platform. This page covers each of them, because a provider evaluating one is evaluating all of them.
Why the numbers stopped working
A managed provider carries a cost structure nobody else in security carries: the alert volume of every customer combined, investigated by one team, under contracts that promise a response time. The industry's answer has been to hire, and there are roughly three and a half million unfilled cybersecurity roles worldwide, so that answer has run out.
The tooling response has been automation of the wrong stage. Alerting was automated years ago. Investigation was not, and investigation is where the hours go: forming a hypothesis, opening five to twelve consoles, pivoting across endpoint, identity, cloud and network, and deciding what actually happened. A summarisation feature makes the write-up faster. It does not remove the analyst from the middle of the work.
What a provider is actually up against
Six pressures, and only one of them is solved by hiring.
Alert volume across every tenant at once
An average SOC receives more than eleven thousand alerts a day, and a provider carries that load multiplied by its customer count.
Analyst hours as the binding constraint
Margin per tenant is set by how many hours each one consumes, and hiring out of the problem is no longer available.
Investigation quality that varies by shift
Depth depends on who picked up the alert, which is difficult to defend when a customer asks why one incident was handled differently from another.
Data separation that has to be real
Tenant isolation is a contractual and often regulatory obligation, not a filter applied to a shared view.
Onboarding time per customer
Every new tenant that takes weeks of connector and content work delays revenue and consumes the same scarce engineers.
Proving the service is working
Customers and their auditors want evidence of what was investigated and why, not a monthly count of tickets closed.
A multi-tenant AI SOC, not a single-tenant product with accounts
Multi-tenancy is the gating requirement for a managed provider, and it is the one most platforms retrofit. Spharaka Sphere™ is a multi-tenant SOC platform by architecture: each customer's telemetry, cases, detections and reporting live in their own boundary, and the provider's team works across all of them from one console.
That distinction is what a customer's risk function tests. A shared store with access filters is defensible until someone asks how it is enforced; tenant-level separation is defensible because there is nothing to enforce.
- Tenant-level data separation, not a filtered view of a shared store
- One console across every tenant for the provider's own analysts
- Per-tenant policy, so each customer's autonomy envelope is their own
- Per-tenant reporting, produced from that customer's evidence alone
- Detection content and threat intelligence improve service-wide without moving customer data
Autonomous investigation, across every tenant at once
Spharaka Sphere™ changes what the analyst is for. AuraXP™, a multi-agent architecture of more than forty specialised autonomous agents, investigates every qualified alert: it forms competing hypotheses, requests the evidence each one needs, queries the telemetry directly, and iterates until it reaches a verdict. The analyst receives a complete investigation rather than a queue position.
For a provider that changes the unit economics rather than trimming them. A manual investigation that runs 60 to 90 minutes becomes a typical autonomous cycle of 60 to 120 seconds, and it runs for every alert on every tenant with the same structured depth, which is also the answer to the question about consistency.
What the platform does for a provider
The capabilities that decide whether an autonomous SOC for MSSPs is deliverable as a service.
Native multi-tenancy
Tenant-level data separation and reporting built into the architecture, so isolation is structural rather than a view filter.
Autonomous investigation at volume
Every qualified alert investigated with evidence collection, validation, correlation and an explainable verdict, across all tenants concurrently.
Consistent investigation depth
The same structured evidence collection and timeline reconstruction on every alert, regardless of which shift receives it.
One workflow, not five consoles
Detection, investigation, case reporting, threat hunting and SOAR response operate through a single platform.
Evidence-linked response
Every recommended action ties back to the specific evidence and affected entities that justified it, which is what a customer review asks for.
Service-ready reporting
SOC-ready investigation reports with summary, malicious behaviour, evidence, timeline, impact and recommended remediation.
An AI MDR platform, not an MDR console
Managed detection and response is sold on outcomes: find it, investigate it, contain it, tell me what happened. An AI MDR platform for MSSPs performs the middle two itself. That is the difference between an autonomous MDR platform and an MDR console, and it is the difference a provider feels in the cost of every contract they sign.
As an MDR automation platform, Sphere covers the full cycle across endpoint, identity, cloud, network and, through Spharaka Signal™, industrial estates. The provider's service wraps around it: the customer relationship, the escalation path, the service levels and the judgement calls.
- Detection across the estate, correlated into events rather than stacked as alerts
- Investigation to a verdict with the evidence chain attached, on every qualified alert
- Containment inside the policy envelope each customer has agreed
- Continuous threat hunting mapped to MITRE ATT&CK across every tenant
- Reporting the customer's auditors can read, produced as the work happens
SOC automation for MSSPs, at the stage that costs money
Most SOC automation for MSSPs automates the parts that were never the bottleneck: ticket creation, enrichment lookups, notification. The hours go into deciding what happened, and deciding is what agents do here.
Automation without governance is how a provider ends up explaining an action to a customer who did not agree to it. AirWatch™ validates every action against the evidence and against that tenant's policy before it runs, records what was done and why, and holds anything higher impact for a named approver on the provider's side or the customer's.
A white label AI SOC your customers see as yours
Providers deliver Spharaka as a white label SOC platform: the service carries the provider's brand, their service levels and their contract, with multi-tenant separation between their customers underneath it. A white label AI SOC is only credible if the reporting is credible, so the investigation reports are generated with summary, malicious behaviour, evidence, timeline, impact and recommended remediation, ready to go out under the provider's name.
Spharaka does not sell the managed service. That is a deliberate choice: a vendor that also sells the service ends up competing with the providers it depends on. See the partner programme for how that works commercially.
The platform behind a SOC-as-a-Service offer
Most buyers of security operations no longer want to staff one. They want SOC as a Service, and they are buying it from providers rather than from platform vendors. A SOC-as-a-Service platform has to support that commercially as well as technically: priced against scope rather than analyst hours, isolated per customer, and able to produce the evidence the customer's auditors will ask for.
That is the offer authorised Spharaka partners build on Sphere: SOC as a Service, co-managed SOC and MDR, in their own regions and under their own agreements.
Signing a customer should not mean hiring for them
Every tenant that takes weeks of connector and content work delays revenue and consumes the same scarce engineers. Five steps, and none of them is a content project.
- 01
Create the tenant
A new customer is a tenant with its own data boundary, its own policy and its own reporting from the moment it exists.
- 02
Connect the sources
Prebuilt connectors across 250+ log sources, so coverage is a configuration rather than a content project.
- 03
Set the autonomy policy
What the platform may do unaided on this customer's estate, and what waits for the provider's approval or the customer's.
- 04
Agents take the queue
Investigation starts on the first alert, at the same depth it will have in month twelve.
- 05
Report from day one
Evidence and reporting are produced as the work happens, so the first customer review has something in it.
A deployment for each customer's rules
A provider's customer base is rarely uniform. One tenant is happy in SaaS, another has a regulator with an opinion about where telemetry lives, and a third cannot send anything anywhere at all. Sphere runs as SaaS, in private cloud, in a sovereign region, hybrid, or fully air-gapped, with SAGE™ served locally so the reasoning happens inside the boundary the customer requires.
It is the same platform in each, which is what keeps a regulated tenant from becoming a second toolchain and a second practice. See choosing a deployment model.
Every analyst operating a tier higher
SAGE™ is Spharaka's cybersecurity model, fine-tuned for security reasoning rather than adapted from a general assistant. Its practical effect on a managed service is levelling: it gives every analyst expert-level investigation guidance regardless of seniority, which is what lets a tier-one analyst close work that previously escalated.
It explains complex multi-signal detections in plain language, so a customer briefing does not require a senior engineer to translate. It interprets obfuscated or decompiled malware for analysts who are not reverse engineers. It produces post-incident reports, executive summaries and compliance documentation, which is the part of managed delivery that consumes senior time and generates no margin.
Where a customer requires it, SAGE™ runs entirely within the deployment boundary, so no investigation data leaves the environment for inference.
Delivering on a stack against delivering on an AI SOC platform
The same service, sold to the same customers, with the costs in different places.
A stack the team integrates
- Tier-one triage
- Analyst hours per tenant, multiplied by every customer you sign.
- Isolation
- A shared store with access filters, defended in every customer review.
- Consistency
- Depends on which shift picked up the alert.
- Onboarding
- Weeks of connector and content work per customer.
- Reporting
- Assembled by senior people who generate no margin doing it.
- Growth
- Sign a customer, hire to cover it.
A multi-tenant AI SOC platform
- Tier-one triage
- Agents investigate every qualified alert across every tenant at once.
- Isolation
- Tenant-level separation in the architecture, with reporting to match.
- Consistency
- The same structured investigation on every alert, every tenant, every hour.
- Onboarding
- Prebuilt connectors across 250+ sources, then policy.
- Reporting
- Produced as the investigation happens, ready for the customer's auditors.
- Growth
- Sign a customer, add a tenant.
MSSP and MDR delivery use cases
Autonomous tier-one triage
Every alert investigated to a verdict before it reaches a person, so analysts open cases that already have their evidence assembled.
Multi-tenant threat hunting
Continuous hypothesis-driven hunts run across every tenant around the clock, never paused by shift changes or backlog.
Cross-tenant intelligence, isolated data
Threat intelligence and detection content improve service-wide while each customer's telemetry stays separated.
Customer incident reporting
Investigation reports with verdict, evidence, timeline and affected entities generated as a by-product of the work.
Escalation with the evidence attached
Anything handed to a customer arrives with the reasoning chain that produced it rather than an alert reference.
Governed response on customer estates
Low-risk actions run automatically; anything higher impact holds for analyst, manager or customer approval.
New tenant onboarding
Prebuilt connectors across 250+ log sources shorten the gap between contract signature and useful coverage.
Sovereign and air-gapped tenants
Customers who cannot send telemetry anywhere are served from an on-premises deployment with local model inference.
Why managed providers build on Spharaka
Margin from autonomy, not headcount
Investigation volume scales without a proportional increase in analyst hours, which is the only lever that changes the economics.
Isolation that survives scrutiny
Tenant-level data separation and reporting are architectural, which is what a customer's risk function actually tests.
Defensible service quality
Every alert receives the same depth of investigation, so consistency is a property of the platform rather than of the rota.
Every analyst operating a tier higher
SAGE™ gives investigation guidance at expert level regardless of the analyst's seniority or specialisation.
One platform to run the service on
Detection, investigation, hunting, reporting and response in one workflow rather than a stack the team has to integrate.
Deployment shaped to the customer
SaaS, private cloud, sovereign cloud, hybrid and fully air-gapped, so a regulated tenant does not need a separate toolchain.
Where a service provider deployment usually starts
For a provider the economics are the argument. Analyst-per-alert does not scale across tenants, which makes automating triage the first thing to prove and the thing that decides whether the margin works.
What makes that possible rather than merely faster is described in the AI SOC capability, and the action half in autonomous response and orchestration. Providers evaluating the category will get more from the AI SOC buyer's guide than from any feature list.
Spharaka is the AI SOC vendor in that arrangement and nothing more: we build and support the platform, and the service, the customer relationship and the brand on the report stay yours. Where a tenant brings an industrial estate, Spharaka Signal™ extends the same console rather than adding a second one. What you can tell your own customers about our posture is set out in the Trust Center.
AI SOC for MSSPs: frequently asked questions
What is an AI SOC for MSSPs?
An AI SOC for MSSPs is a security operations platform in which AI agents perform the monitoring, triage, investigation and response work across every tenant a provider serves, while the provider's analysts set policy, review conclusions and handle the decisions that need judgement. The difference from an enterprise AI SOC is multi-tenancy: one console for the provider, separated data for each customer.
What makes an AI SOC platform for MSSPs different from a SIEM?
A multi-tenant SIEM collects and correlates for each customer and hands the result to an analyst. An AI SOC platform for MSSPs carries each event on through investigation and response, so the provider's cost per tenant is not set by how many alerts a person can work in a shift. Spharaka Sphere™ provides AI SIEM, AI SOAR and AI UEBA in one platform.
Is Spharaka Sphere genuinely multi-tenant?
Yes. The architecture is natively multi-tenant, with tenant-level data separation and reporting rather than a shared store with access filters, which is what a service provider needs when isolation is a contractual obligation.
Can we white label the AI SOC?
Yes. Providers deliver a white label SOC platform under their own brand, with multi-tenant separation between their customers. Investigation reports are generated with summary, malicious behaviour, evidence, timeline, impact and recommended remediation, and presentation and branding are covered during onboarding.
What is an AI MDR platform?
An AI MDR platform is the software a managed detection and response service runs on, where the detection and the investigation are performed by AI agents rather than by a tier-one queue. For a provider, an autonomous MDR platform changes the unit economics of the service: the investigation stage stops consuming analyst hours per customer.
How does autonomous investigation change an MDR provider's economics?
It removes analyst hours from the stage that consumes most of them. A manual alert investigation typically runs 60 to 90 minutes; the autonomous cycle typically runs 60 to 120 seconds, and it runs on every qualified alert across every tenant, so investigation volume scales without a matching increase in headcount.
What does SOC automation for MSSPs actually automate?
Alerting was automated years ago; what an MSSP needs automated is investigation. Spharaka automates evidence collection, correlation across endpoint, identity, cloud and network, verdict and explanation, report generation and the low-risk response actions a customer has pre-approved. Escalation, policy and customer decisions stay with people.
Do analysts still have a role?
Yes, a different one. AI handles speed and scale; humans handle direction and accountability. Analysts receive complete reasoned incident narratives with the evidence chain that produced them, set the risk appetite and escalation thresholds, and approve the actions that warrant approval.
How long does it take to onboard a new customer?
Prebuilt connectors across 250+ log sources including EDR, firewall, proxy, identity, email, cloud, SaaS, database, DNS, VPN, WAF and OT mean coverage is typically useful within weeks rather than quarters.
Can we serve customers who require data residency or air-gapped operation?
Yes. Sphere runs on-premises, in private cloud, in a secure enclave or fully air-gapped, with SAGE™ served locally so no investigation data leaves the customer environment.
What stops an autonomous action from affecting a customer without consent?
AirWatch™ validates every action against verified evidence and the policy set for that tenant. Low-risk and pre-approved actions can run automatically; high-impact actions require analyst, manager or customer approval before execution.
Does it replace the SIEM we deliver the service on?
It can. Sphere collapses SIEM, SOAR, XDR, EDR and UEBA into one platform, and providers typically run it alongside an existing stack during a phased migration rather than switching every tenant at once.
Can we sell SOC as a Service on it?
Yes. A SOC-as-a-Service platform is exactly what a provider needs to sell the outcome rather than the tooling, and authorised Spharaka partners deliver SOC as a Service, co-managed SOC and MDR on Spharaka Sphere™ under their own agreements.
How does it handle OT customers?
Spharaka Signal™ covers industrial and ICS estates passively and feeds the same investigation surface, so an OT customer does not require a separate practice and a separate console.
Is Spharaka an AI SOC provider that competes with us?
No. Spharaka is an AI SOC vendor and platform provider: it builds Spharaka Sphere™ and supports the providers running services on it. The managed service, the customer contract and the reporting brand belong to the provider. Buyers who ask us for a managed service are introduced to an authorised partner.
Where to go next
Partner Programme
How providers sell and deliver SOC as a Service on Spharaka.
Alert Triage Automation
The capability the multi-tenant economics actually rest on.
AI SOC Buyer's Guide
A vendor-neutral framework, useful when you are the one being evaluated.
Trust Center
What you can tell your own customers about our posture.
See a multi-tenant AI SOC on your own tenants
A walkthrough on your own estate: what it concludes, what it does on its own, and the reporting your customers would receive.