AI SOC Platform for Autonomous Security Operations
Spharaka Sphere™ is an AI SOC platform: an AI-native, autonomous security operations center that detects, investigates and responds to threats at machine speed, with people kept on the decisions that carry consequence. One platform in place of a stitched-together SIEM, SOAR, UEBA and XDR stack.
What is an AI SOC?
An AI SOC is a security operations center in which AI agents, rather than analysts working a queue, carry out the core work of monitoring, detection, investigation and response. People set the policy, review the conclusions and approve the actions that carry real consequence. The term covers the AI-powered SOC, the AI-driven SOC and the AI-based SOC, and at its most capable, the autonomous SOC and the agentic SOC.
Whether it is written as an AI security operations center or an AI security operations centre, the idea is the same, and so is the problem it answers: attackers now move at machine speed, and a security operation that waits for a person to open each alert cannot keep up.
An AI SOC platform, sometimes called an AI security operations platform, is the software that runs one. Spharaka Sphere™ is an AI SOC platform, and Spharaka Networks is the AI SOC company that builds it: as an AI SOC solution for enterprises running their own security operations, and as AI SOC software that managed security providers deliver to their customers as a service.
How an AI SOC works
An AI SOC runs one loop continuously, rather than waiting for an analyst to open a ticket. Steps three to six are the ones a conventional SOC performs by hand, and they are where the hours go.
Collect
Telemetry arrives from endpoints, network sensors, identity providers, cloud control planes, SaaS audit logs and, where Spharaka Signal is deployed, industrial networks. It is normalised, enriched and tagged on arrival rather than at query time, so asking a question later does not cost more the more you kept.
Baseline
AI UEBA holds a behavioural baseline for every user and entity: which systems an account touches, at what hours, over which protocols, from where. That baseline is what makes an action that is procedurally permitted but behaviourally wrong visible at all.
Correlate
Signals are joined into one security event before an alert exists. A failed policy check, an unusual authentication and a process launch on the same host in the same window are one event, not three alerts for someone to reconcile. This is the step that reduces volume rather than reordering it.
Investigate
AuraXP agents work the event as a case: pulling the surrounding telemetry, testing what else the same identity or host did, enriching against threat intelligence and reconstructing the sequence. SAGE reasons over the result and reaches a conclusion, with the evidence chain attached.
Decide and act
The conclusion is checked against AirWatch policy. Actions inside the autonomous envelope execute immediately through AI SOAR: isolating a host, disabling a session, revoking a token, blocking a route. Actions outside it are staged with the evidence and wait for a named approver.
Record
Every action taken and every action declined is written with the reasoning and evidence behind it. That record makes an autonomous decision reviewable afterwards, and it is the same record a regulator or internal audit asks for.
AI SOC vs traditional SOC
Not a comparison of feature lists. These are the six places where an autonomous SOC solution organises the work itself differently, and they are why the outcome differs.
AI SOC vs SIEM
A SIEM collects logs, normalises them and raises alerts when correlation rules match. It answers the question of what happened, and leaves the question of what it means to an analyst. An AI SOC includes that collection and correlation, through AI SIEM, and carries each event on through investigation and response.
More in AI SIEM vs traditional SIEM and the AI SIEM migration guide.
AI SOC vs SOAR
SOAR executes playbooks that a person wrote in advance, and it does that quickly and consistently for incidents that match a known shape. An AI SOC decides what the response should be for the incident in front of it, including the ones nobody wrote a playbook for, and still calls existing playbooks where they remain the right answer.
More in autonomous cyber defence vs SOAR and AI SOAR.
AI SOC vs XDR
XDR correlates detections across endpoint, network and cloud and hands an analyst an incident to investigate. That is a real improvement on separate consoles, but the investigation and the decision are still a person's. An AI SOC investigates the incident itself and acts on its conclusion within policy.
More in Beyond XDR.
AI-powered threat detection and monitoring
AI SOC monitoring covers the whole estate continuously rather than the fraction of alerts people reach. AI SOC threat detection starts from behaviour: a stolen credential produces a successful login, not a failed one, so a rule has nothing to match, while a baseline for that user sees the wrong hour, the wrong place and the wrong device.
- Behavioural baselines for every user and entity through AI UEBA
- Correlation into one event before an alert is raised
- Threat intelligence enrichment, freshness-weighted and mapped to your assets
- Coverage across endpoint, network, identity, cloud, SaaS and industrial OT
- Detections mapped to MITRE ATT&CK tactics and techniques
See AI UEBA and cyber threat intelligence.
AI SOC investigation
Investigation is where a conventional SOC loses its time: an analyst opens several tools, forms a theory and validates it by hand. In an AI SOC, AuraXP™ agents work every event as a case. They pull the surrounding telemetry, test what else the same identity or host did, enrich against threat intelligence and reconstruct the sequence. SAGE™, Spharaka's cybersecurity-specific model, reasons over the result and reaches a conclusion with the evidence chain attached.
- Every event investigated, not only the ones that reach the top of a queue
- A conclusion in plain language, not a score
- An evidence chain an analyst, auditor or regulator can follow
- Reasoning that runs inside your boundary, not at an external model API
More in AI-assisted vs AI-driven investigation and the SAGE™ AI model.
Autonomous threat hunting
AI SOC threat hunting does not wait for an alert. Hypotheses are generated continuously from threat intelligence, changes in the environment and earlier findings, and tested against live telemetry. People review outcomes rather than writing queries, and a confirmed finding is promoted to a standing detection so the next occurrence is caught automatically.
See autonomous threat hunting and what autonomous threat hunting is.
AI incident response and orchestration
AI SOC incident response runs through AI SOAR, inside a policy envelope that AirWatch™ enforces. Actions the policy allows execute immediately: isolating a host, disabling a session, revoking a token, blocking a route. Actions outside it are staged with the evidence and the recommended step, and wait for a named approver. AI SOC orchestration reaches endpoints, identity providers, firewalls, cloud APIs, ticketing and an existing SOAR where one is in place.
Worked example.
Twenty three minutes from first signal to a revoked session, at an hour when the alternative was a queue entry waiting for the morning shift. See AI SOAR and dynamic playbooks.
AI SOC automation and agentic security operations
AI SOC automation is not a bigger playbook library. Automation follows rules someone wrote; agentic security operations run agents that plan the next step, call a tool against a real system, observe the result and decide again, until the goal is met or the policy envelope stops them. That is the difference between an AI SOC automation platform and a playbook engine, and it is what lets an agentic SOC platform handle incidents nobody anticipated. Sphere is an agentic cybersecurity platform in that sense: its agents investigate and act, inside limits the customer sets.
- Triage, enrichment and correlation, for every alert
- Investigation to a conclusion, with evidence
- Containment inside the envelope the customer defines, and staged approval outside it
- Case records, handover briefs and audit evidence, written as decisions are made
More in agentic AI vs SOAR automation and alert triage automation.
AI SOC architecture
The AI SOC technology in Spharaka Sphere™, an autonomous SOC platform, is built as layers that share one context, from the governance boundary down to the sensors.
Telemetry it ingests
- Endpoint and server process, file and registry activity
- Network flow, DNS and east-west traffic
- Identity and directory authentication events
- Cloud control plane and workload audit logs
- SaaS audit trails, email and web gateway telemetry
- Industrial protocol traffic through Spharaka Signal™
Systems it acts on
- Endpoint agents, including Spharaka EdgeProtect™
- Identity providers, for session and credential revocation
- Firewalls and network access control
- Cloud provider APIs for workload and role actions
- Ticketing and ITSM for approvals and handover
- An existing SOAR, where one is in place
What it enriches with
- Commercial and open-source threat intelligence
- Malware family, campaign and adversary attribution
- Vulnerability data linked to the assets exposed
- Asset criticality and ownership from the CMDB
- MITRE ATT&CK tactic and technique mapping
Around two hundred enterprise integrations ship with the platform. Where an existing SIEM, EDR or SOAR is staying, Sphere integrates with it rather than requiring its removal.
AI SOC software for enterprise security operations
Buying AI SOC software is a different exercise from understanding what an AI SOC is. At that point the questions are concrete: where it runs, what it ingests, what it connects to, what it does on its own, which model does the reasoning, and whether the evidence stays where your regulator expects it. This is the specification of Spharaka Sphere™ as AI security operations software, in the order a buyer tends to ask for it.
- Deployment
- SaaS, on-premises in your own data centre, hybrid, or air-gapped on a pre-configured appliance. The same software in each, not a reduced build for the disconnected case. See deployment.
- Log ingestion
- Agents, syslog, cloud-native streams, API pulls and network sensors. Parsing, normalisation and enrichment happen on ingest rather than at query time, and a source without a parser is captured and classified rather than dropped.
- Retention
- Long-term retention that stays queryable, so asking a question of last quarter does not mean rehydrating an archive first.
- Integrations
- Around two hundred enterprise integrations: endpoint and EDR, identity providers, firewalls and network access control, cloud platforms, SaaS and email, ITSM and ticketing, and an existing SIEM or SOAR where one is staying in place.
- Detection
- AI SIEM correlation, AI UEBA behavioural baselines and threat intelligence running over one copy of the telemetry, mapped to MITRE ATT&CK.
- Investigation
- Every event investigated rather than a sampled queue: hypotheses tested against the evidence, a written conclusion, and the reasoning behind it kept with the case.
- Threat hunting
- Continuous, hypothesis-driven hunting across live and historical telemetry, rather than a scheduled exercise that depends on who is free. See autonomous threat hunting.
- Response
- Containment and remediation reaching endpoints, identity providers, firewalls, cloud provider APIs and ticketing, inside a policy envelope you define. See AI SOAR.
- Case management
- Cases assembled by the agents that worked them: timeline, evidence, affected assets, actions taken and what is waiting on a person, with collaboration, SLA tracking and handover to the ITSM tool you already use.
- AI model
- SAGE™, a cybersecurity-specific model served inside the deployment boundary, rather than a general-purpose model called at an external provider.
- Data residency
- Telemetry, evidence and the reasoning over both stay in the jurisdiction you choose, including on India-resident cloud infrastructure, because the model runs where the platform runs.
- Multi-tenancy
- Real data separation between tenants with one console across them, for managed providers and for groups running several subsidiaries or regions.
- APIs
- Integration points for pushing telemetry in, pulling cases, evidence and reporting out, and triggering platform actions from the systems you already run.
- Governance
- AirWatch™ checks every agent action against policy before it executes and records an evidence chain of what was done and why, with administrator review and rollback.
Two questions decide most AI SOC software evaluations, and neither appears on a feature grid: where the reasoning happens, and what the platform is allowed to do without asking. The AI SOC buyer's guide sets out how to test both on your own data during a trial.
AI SOC deployment
Where an AI SOC runs decides what it may reason about. Sphere runs the same platform in each model below, with the AI reasoning inside the boundary you choose rather than at an external model provider.
See choosing a deployment model and Sphere™ on-premises. For the Indian market, see the Made in India cybersecurity platform.
AI SOC for enterprises
An enterprise AI SOC platform, or any AI security operations solution a large organisation adopts, has to do more than detect well. It has to hold up in front of a regulator, keep sensitive telemetry under the organisation's control, and work alongside the tools already in place. Sphere is built for banks, government, healthcare, energy, manufacturing and defence, where the location of the reasoning matters as much as its quality: an AI SOC platform for enterprises whose data cannot leave their control.
- Full data sovereignty, with customer-isolated retrieval and a private cybersecurity model
- An auditable record of every autonomous decision
- Coverage of IT and OT from one platform
- Integration with the SIEM, EDR and SOAR you already run
See security by industry and the AI SOC buyer's guide.
AI SOC for MSSPs and MDR providers
For a managed security provider, an AI SOC changes the economics of the service. An average SOC receives more than eleven thousand alerts a day, and a provider carries that load multiplied by its customer count. When agents investigate every alert across every tenant, margin stops being set by how many analyst hours each customer consumes.
- Multi-tenant, with real data separation between customers
- One console for the provider's team across every tenant
- Consistent investigation quality, whichever shift picks up the alert
- Evidence of what was investigated and why, for each customer's auditors
Providers deliver AI SOC services on AI security operations software built for multi-tenancy from the start. See AI SOC for MSSPs and MDR and the partner programme.
Spharaka as an AI SOC vendor and platform provider
Spharaka Networks is an AI SOC vendor: a cybersecurity technology company that builds the platform an AI security operations centre runs on, rather than reselling someone else's. Spharaka Sphere™ is our own product, and the three pieces underneath it are our own engineering: the SAGE™ cybersecurity model, the AuraXP™ agent fabric and the AirWatch™ governance layer.
Vendor and provider are used interchangeably in this market, and they name two different things that are worth separating before a procurement conversation starts.
What that makes Spharaka, put plainly as an AI SOC company:
- An AI SOC technology company: SAGE™, AuraXP™ and AirWatch™ are built here, not a general-purpose model given a security prompt
- An AI SOC platform provider to enterprises buying directly and to managed providers delivering a service on it
- One vendor across IT and OT, through Spharaka Sphere™, Spharaka Signal™ and Spharaka EdgeProtect™
- A platform you can run where your regulator expects it, with the reasoning inside your own boundary
- Sold directly and through authorised partners, so the service business does not compete with the people selling it
More on the company behind the platform on the company page, on delivery through partners on the partner page, and for Indian buyers on the Made in India cybersecurity platform.
How to evaluate an AI SOC vendor
Every AI SOC vendor describes its product as an AI SOC, and every autonomous security operations platform claims to reason. These four tests separate the platforms that reason from the ones that rank, and they should run against your own data rather than a demonstration tenant.
What did it conclude, not what did it score?
Ask for its conclusion on a real incident in plain language, with the evidence. A confidence score and a ranked queue is an assisted product describing itself as autonomous.
What did it do without being asked?
Count the autonomous actions in the trial and the time to each. If the answer is zero, it is a better console, which is a different purchase at a different price.
What happened on the case it got wrong?
Can you see which evidence it weighted and what the policy allowed? A platform that cannot explain a wrong answer cannot be trusted with a right one.
Does it degrade when disconnected?
Run part of the trial on-premises or air-gapped. Reasoning that happens at an external API leaves log storage and a rules engine when the link is cut.
AI SOC: frequently asked questions
What is an AI SOC?
An AI SOC is a security operations center in which AI agents carry out the core work of monitoring, detection, investigation and response, while people set policy, review conclusions and approve high-impact actions. It is also called an AI-powered, AI-driven or AI-based SOC, and at its most advanced an autonomous or agentic SOC.
What does an AI SOC platform do?
An AI SOC platform is the software that runs an AI security operations center. It collects telemetry, builds behavioural baselines, correlates signals into events, investigates each event to a conclusion, responds within a defined policy and records every decision. Spharaka Sphere™ is an AI SOC platform that does this as one system rather than a set of separately licensed tools.
What is AI SOC software?
AI SOC software is the product an organisation licenses to run an AI security operations center: ingestion and retention of security telemetry, integrations with the tools already in place, AI detection, investigation, threat hunting, incident response, case management and the governance record over all of it. Spharaka Sphere™ provides these as one platform, deployable as SaaS, on-premises, hybrid or air-gapped.
Does the AI SOC software include case management?
Yes. Cases are assembled by the agents that worked them, with the timeline, the evidence, the affected assets, the actions taken and whatever is waiting on a person. Collaboration, SLA tracking and reporting are built in, and cases can be handed to the ITSM or ticketing system already in use.
Is an AI security operations centre the same as an AI security operations center?
Yes. Centre is the British and Indian English spelling and center the US spelling. Both describe the same thing: a security operations function in which AI agents perform the monitoring, investigation and response work that analysts traditionally did by hand.
What is the difference between an AI-powered SOC and an autonomous SOC?
An AI-powered SOC uses AI to help analysts: summarising alerts, enriching them and suggesting next steps, while a person still investigates and decides. An autonomous SOC runs the investigation and the response itself inside a policy envelope, and brings people in for the decisions that policy reserves for them.
What is an agentic SOC?
An agentic SOC, or agentic AI SOC, is an AI SOC built from AI agents that plan an investigation, call tools against real systems, observe the results and decide the next step, rather than executing a fixed playbook. Spharaka's AuraXP™ runs more than 40 specialised agents as a virtual SOC team inside Spharaka Sphere™.
Does an AI SOC replace a SIEM?
An AI SOC includes the SIEM's job, collection and correlation, and carries each event on through investigation and response. Spharaka provides AI SIEM, AI SOAR and AI UEBA in one AI SOC platform. Where an existing SIEM is staying in place, Sphere integrates with it rather than requiring its removal.
How is an AI SOC different from SOAR?
SOAR executes playbooks that a person wrote in advance for incidents someone anticipated. An AI SOC reasons about the incident in front of it and decides what the response should be, generating the playbook per incident within policy. It can still invoke existing playbooks where they remain the right, deterministic answer.
How is an AI SOC different from XDR?
XDR correlates detections across endpoint, network and cloud and hands an analyst an incident to investigate. An AI SOC investigates that incident itself, reaches a conclusion with evidence, and acts on it within policy. Spharaka's platform unifies SIEM, SOAR, XDR, UEBA and EDR so the investigation shares one context.
Does an AI SOC replace security analysts?
No. It changes what they spend their time on. Agents investigate every event, so analysts stop triaging queues and start reviewing conclusions, approving the actions policy reserves for them, tuning that policy and handling the genuinely ambiguous cases.
Can the AI SOC platform be deployed on-premises or in India?
Yes. Spharaka Sphere™ runs as SaaS, including on India-resident cloud infrastructure, on-premises in your own data centre, in hybrid deployments, and air-gapped on a hardware appliance. The AI reasoning runs inside the boundary you choose, which is what makes it suitable for regulated industries.
Is Spharaka an AI SOC provider for MSSPs?
Yes. Sphere is multi-tenant, with real data separation between customers and one console for the provider's team. Managed security providers use it to deliver AI SOC services and MDR, with every alert investigated across every tenant.
Is Spharaka an AI SOC vendor?
Yes. Spharaka Networks is an AI SOC vendor and cybersecurity technology company. It builds Spharaka Sphere™ together with the SAGE™ cybersecurity model, the AuraXP™ agent fabric and the AirWatch™ governance layer inside it, rather than reselling another company's platform. Enterprises license it directly, and authorised partners deliver it as a managed service.
What is the difference between an AI SOC vendor and an AI SOC provider?
An AI SOC vendor, sometimes called an AI SOC company or AI SOC platform provider, builds and supports the software an AI security operations center runs on. An AI SOC service provider runs the security operation for you on that software, as SOC as a Service, a managed SOC or MDR. Spharaka is the vendor, and authorised Spharaka partners are the service providers.
How should we evaluate an AI SOC vendor?
Run the trial on your own data and ask four things: what the platform concluded on a real incident, not what it scored; what it did without being asked; why it got a case wrong; and whether its reasoning still works when disconnected. A vendor whose platform cannot answer those is selling an assisted console.
The capabilities inside the AI SOC
AI SIEM
Collection, correlation and prioritisation.
AI SOAR
Playbooks generated per incident, and autonomous response.
AI UEBA
Behavioural baselines for every identity and entity.
Autonomous Threat Hunting
Continuous hunts mapped to MITRE ATT&CK.
Cyber Threat Intelligence
Enrichment that reaches decisions.
Autonomous Cyber Defence Platform
The category the AI SOC belongs to.
See the Spharaka AI SOC on your own data
A walkthrough on your own estate: what it concludes, what it does on its own, and the record it keeps.