Industries - MSSP & MDR
Multi-tenant investigation at machine speed, with every customer's data kept apart.
A managed provider's margin is analyst hours per tenant, and every tool that adds a console adds hours. Spharaka Sphere™ investigates every qualified alert autonomously across every tenant, producing a verdict, an evidence trail and an attack timeline, on a natively multi-tenant architecture with tenant-level data separation and reporting.
Cybersecurity Landscape
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.
An average SOC receives more than eleven thousand alerts a day, and a provider carries that load multiplied by its customer count.
Margin per tenant is set by how many hours each one consumes, and hiring out of the problem is no longer available.
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.
Tenant isolation is a contractual and often regulatory obligation, not a filter applied to a shared view.
Every new tenant that takes weeks of connector and content work delays revenue and consumes the same scarce engineers.
Customers and their auditors want evidence of what was investigated and why, not a monthly count of tickets closed.
Autonomous Cyber Defence
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.
Tenant-level data separation and reporting built into the architecture, so isolation is structural rather than a view filter.
Every qualified alert investigated with evidence collection, validation, correlation and an explainable verdict, across all tenants concurrently.
The same structured evidence collection and timeline reconstruction on every alert, regardless of which shift receives it.
Detection, investigation, case reporting, threat hunting and SOAR response operate through a single platform.
Every recommended action ties back to the specific evidence and affected entities that justified it, which is what a customer review asks for.
SOC-ready investigation reports with summary, malicious behaviour, evidence, timeline, impact and recommended remediation.
The AI Cybersecurity Analyst
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.
Natural language investigations
Guided AI-assisted analysis
Root cause reconstruction
Contextual threat intelligence
Accelerated analyst productivity
Executive-friendly explanations
Industry Use Cases
Every alert investigated to a verdict before it reaches a person, so analysts open cases that already have their evidence assembled.
Continuous hypothesis-driven hunts run across every tenant around the clock, never paused by shift changes or backlog.
Threat intelligence and detection content improve service-wide while each customer's telemetry stays separated.
Investigation reports with verdict, evidence, timeline and affected entities generated as a by-product of the work.
Anything handed to a customer arrives with the reasoning chain that produced it rather than an alert reference.
Low-risk actions run automatically; anything higher impact holds for analyst, manager or customer approval.
Prebuilt connectors across 250+ log sources shorten the gap between contract signature and useful coverage.
Customers who cannot send telemetry anywhere are served from an on-premises deployment with local model inference.
Why Spharaka
Investigation volume scales without a proportional increase in analyst hours, which is the only lever that changes the economics.
Tenant-level data separation and reporting are architectural, which is what a customer's risk function actually tests.
Every alert receives the same depth of investigation, so consistency is a property of the platform rather than of the rota.
SAGE™ gives investigation guidance at expert level regardless of the analyst's seniority or specialisation.
Detection, investigation, hunting, reporting and response in one workflow rather than a stack the team has to integrate.
SaaS, private cloud, sovereign cloud, hybrid and fully air-gapped, so a regulated tenant does not need a separate toolchain.
Deployment
Spharaka Sphere™ is engineered for the operational realities of modern enterprises. Whether your infrastructure is fully in the cloud, on-premises, air-gapped for regulatory reasons, or spread across hybrid environments, Sphere deploys where your data lives and where your security teams operate.
Elastic, multi-region cloud deployment for born-in-cloud enterprises.
Full control within your data centre for strict data residency needs.
Isolated environments for regulated, classified, or critical operations.
Unified visibility across cloud, on-prem, and edge in one platform.
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.
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.
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.
Investigation reports are generated with summary, malicious behaviour, evidence, timeline, impact and recommended remediation, and multi-tenancy includes tenant-level reporting. Presentation and branding are covered during onboarding.
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.
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.
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.
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.
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.
Book a personalized demonstration of Spharaka Sphere™ and see how AI-native security operations transform detection, investigation, and response for your organization.