AI SOC Platform for Autonomous Security Operations
Spharaka delivers an AI-native, autonomous Security Operations Center that detects, investigates, and responds to security events at machine speed. Modernize your SOC with an AI-powered platform built for enterprise scale.
An AI-Native, Autonomous SOC for the Enterprise
Spharaka's AI SOC platform unifies AI SIEM, AI SOAR, and AI UEBA into a single autonomous SOC. Analysts get an AI Security Copilot for triage, investigation, and decision support, while the platform autonomously closes high-confidence cases end to end.
- AI-powered detection across endpoints, network, identity, cloud, and SaaS
- Autonomous investigation with root cause analysis and evidence chains
- Autonomous response and remediation through dynamic AI SOAR playbooks
- Continuous AI threat hunting mapped to MITRE ATT&CK
- AI Security Copilot for Tier 1, Tier 2, and Tier 3 analyst workflows
Built for SOC Modernization and Scale
Replace stitched-together SIEM, SOAR, UEBA, and XDR tools with a single AI-driven SOC platform that learns your environment and gets better every day.
- Unified data, detection, and response across the full attack surface
- Reduces mean time to detect (MTTD) and mean time to respond (MTTR)
- Cuts alert fatigue with AI correlation and threat prioritization
- Deployable as cloud, on-premise, or hybrid AI SOC
- Full data sovereignty with private SAGE™ AI model and AuraXP fabric
Core AI SOC Capabilities
- AI SIEM with log analytics, correlation engine, and AI correlation
- AI SOAR with playbooks, workflow automation, and security orchestration
- AI UEBA for user and entity behaviour analytics and insider threat detection
- AI-powered Incident Response with case management and timelines
- Autonomous Threat Hunting and Cyber Threat Intelligence enrichment
From a log line to a closed case, without a queue in the middle
The loop below runs continuously rather than being triggered by an analyst opening 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 the cost of asking a question later does not scale with how much you kept.
Baseline
AI UEBA holds a behavioural baseline for every user and every entity: which systems this account touches, at what hours, using which protocols, from where. That baseline is what makes an action which is procedurally permitted but behaviourally wrong visible at all.
Correlate
Signals are joined into a 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 afterwards. 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 to it.
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 the recommended step, and wait for a named approver.
Record
Every action taken and every action declined is written with the reasoning and the evidence that produced it. That record is what makes an autonomous decision reviewable afterwards, and it is the same record a regulator or an internal audit asks for.
What the SOC sees, and what it can reach
A security operation is limited by two things: what it can observe and what it can act on. Both lists matter more than the detection logic sitting between them.
Telemetry it ingests
- Endpoint and server process, file and registry activity
- Network flow, DNS and east-west traffic
- Identity provider and directory authentication events
- Cloud control plane and workload audit logs
- SaaS application audit trails
- Email, web proxy and 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
- Existing SOAR, where one is already in place
What it enriches with
- Commercial and open-source threat intelligence feeds
- Malware family, campaign and adversary attribution
- Vulnerability data linked to the specific 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 in place, Sphere integrates with it rather than requiring its removal.
How this differs from a conventional SOC
Not a comparison of feature lists. These are the six places where the work itself is organised differently, and they are the reason the outcome differs.
| Aspect | Conventional SOC | Spharaka |
|---|---|---|
| Where signals meet | After each tool has raised its own alert, in an analyst's head or a correlation rule written months ago. | Before an alert exists, in one data layer holding one copy of the telemetry. |
| Who investigates | A Tier 1 analyst working a queue, escalating what they cannot resolve inside the time they have. | Agents investigate every event to a conclusion. Analysts see conclusions and the cases that need judgement. |
| How response is defined | Playbooks written in advance for incidents someone anticipated, which age as the estate changes. | Playbooks generated per incident from the case in front of them, bounded by policy rather than by author. |
| What limits throughput | Headcount. Volume rises, the backlog grows, and tuning trades missed detections for a shorter queue. | Policy. Volume rises and the loop absorbs it; what needs a human is what policy says needs a human. |
| Where evidence comes from | Reconstructed after the fact, usually during an audit or a post-incident review, from partial records. | Written as the decision is made, including for the actions that were declined. |
| What happens out of hours | A reduced shift triages and escalates. Most containment waits for the morning. | The loop is the same at 03:00 as at 15:00. Only the approval steps wait for a person. |
One intrusion, from first signal to containment
A stolen credential used against a VPN concentrator out of hours. Nothing in the first four minutes would raise an alert in a rule-based SOC, because every individual action is permitted.
A successful VPN authentication for a finance user at 02:14 local time, from an ASN the organisation has never seen. Valid credentials, valid MFA response. No rule fires.
AI UEBA scores the authentication against that user's own baseline: wrong hour, wrong geography, wrong device. On its own this is a low-confidence anomaly, of which there are dozens a day.
The same session enumerates directory group membership and reaches two file servers the account has access to but has not touched in ninety days. Correlation joins the authentication and the access into one event.
AuraXP agents work the case: they pull the account's history, test whether peers in the same role show the pattern, check the source ASN against threat intelligence, and find it associated with a credential-broker campaign.
SAGE reaches a conclusion, credential compromise with active reconnaissance, and attaches the evidence chain: the baseline deviation, the access sequence, the enrichment and the peer comparison.
AirWatch policy permits session revocation and account suspension for this confidence level and this asset class. Both execute. Isolating the file servers is outside the envelope, so it is staged for approval with the evidence attached.
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. The staged action was approved by the on-call lead eleven minutes later from the case itself, without reconstructing what had happened.
What to test before you believe any of this
Every vendor in this category describes itself as an AI SOC. These are the four tests that separate the platforms that reason from the ones that rank, and they should be run against your own data rather than a demonstration tenant.
What did it conclude, not what did it score?
Ask for the platform's conclusion on a real incident in plain language, with the evidence attached. A confidence score and a ranked queue is an assisted product describing itself as an autonomous one. A conclusion is a statement you can agree or disagree with.
What did it do without being asked?
Count the actions taken autonomously during the trial and the time from detection to each one. If the answer is zero, the platform is a better console, which may be worth buying but is a different purchase at a different price.
What happened on the case it got wrong?
Every platform will misjudge something in a trial. The useful question is whether you can see why: which evidence it weighted, what the policy allowed, and whether the record is good enough to tune from. A platform that cannot explain a wrong answer cannot be trusted with a right one.
Does it degrade when disconnected?
If the deployment will be on-premises or air-gapped, run part of the trial that way. A platform whose reasoning happens at an external API becomes log storage with a rules engine the moment the link is cut, and the demonstration will not show you that.
Frequently asked questions
What is an AI SOC platform?
An AI SOC platform is an AI-native Security Operations Center that uses agentic AI, machine learning, and a cybersecurity LLM to automate detection, investigation, and response. Spharaka's AI SOC platform replaces fragmented SIEM, SOAR, UEBA, and XDR tools with one autonomous SOC.
How is an Autonomous SOC different from a traditional SOC?
A traditional SOC depends on human analysts to triage alerts and run playbooks. An autonomous SOC uses AI agents to investigate, decide, and remediate security events end to end, with human oversight for high-impact decisions only.
Does Spharaka's AI SOC replace my SIEM and SOAR?
Yes. Spharaka provides AI SIEM, AI SOAR, and AI UEBA in one unified AI SOC platform, removing the need for separately licensed and stitched-together tools.
Is the AI SOC platform suitable for regulated industries?
Yes. Spharaka supports cloud, on-premise, and hybrid deployment with full data sovereignty, customer-isolated RAG, and a private cybersecurity LLM, making it suitable for BFSI, government, healthcare, and critical infrastructure.
How does the AI SOC reduce analyst workload?
The platform autonomously correlates alerts, runs investigations, generates evidence chains, and executes remediation playbooks. Analysts focus on high-value decisions instead of repetitive triage.
Explore related capabilities
AI SIEM
AI-native log analytics, correlation engine, and threat prioritization.
AI SOAR
Autonomous response, playbooks, and security orchestration.
AI UEBA
User and entity behaviour analytics with insider threat detection.
Autonomous Threat Hunting
Continuous AI-driven threat hunting mapped to MITRE ATT&CK.
Cyber Threat Intelligence
Multi-source CTI with IOC enrichment and threat context.
SAGE™ AI Model
Spharaka's proprietary Cybersecurity SLM that powers this capability.
See Spharaka AI SOC in action
Discover how Spharaka's AI-native, autonomous cyber defence platform modernises your security operations.