AI SIEM, AI-Native Log Analytics and Correlation
Replace legacy SIEM with Spharaka's AI SIEM, an AI-native platform that ingests, correlates, and prioritizes security events across your entire environment using a cybersecurity LLM and behavioural AI.
AI-Driven Log Collection and Analytics
Spharaka AI SIEM ingests logs and telemetry from endpoints, network, identity, cloud, SaaS, and OT, then applies AI correlation to surface what truly matters.
- Universal log collection across hybrid and multi-cloud estates
- AI log analytics with normalization, enrichment, and tagging
- AI correlation engine that links signals into security events
- Threat prioritization powered by the SAGE™ AI Model
- Root cause analysis with evidence chains and timelines
- AI investigation that drafts hypotheses and validates them autonomously
Built for the AI SOC
AI SIEM is the analytics core of Spharaka's AI SOC platform, feeding AI SOAR, AI UEBA, and Autonomous Threat Hunting with high-fidelity signals.
- Native integration with AI SOAR for closed-loop autonomous response
- Shared context with AI UEBA and autonomous detection for unified decisions
- Detection engineering with MITRE ATT&CK mapping
- AI-generated detection rules and continuous tuning
- Data sovereignty with customer-isolated storage and processing
What happens between a log line arriving and an event existing
A legacy SIEM does most of this work at query time, which is why asking a question of last quarter is slow and expensive. Here it happens on ingest, once.
Collect
Agents, syslog, cloud-native streams, API pulls and network sensors deliver telemetry from hybrid and multi-cloud estates. Collection is designed for the case where nobody has a complete inventory, so unknown sources are captured and classified rather than dropped for lacking a parser.
Normalise
Every source is mapped to one schema on arrival. This is the step legacy SIEMs defer, and deferring it is why a detection written against one vendor's firewall silently stops working when the firewall is replaced. One schema means a detection is written against the behaviour, not against the log format.
Enrich
Identity, asset ownership, criticality, geolocation, threat intelligence reputation and MITRE ATT&CK context are attached at ingest. An analyst or an agent reading the event later does not have to go and fetch any of it, which is most of what makes manual triage slow.
Correlate
Related signals are joined into a security event. This is not a rule matching a pattern so much as a judgement that these observations describe one thing happening. It is the step that reduces the number of items a human is asked to look at, rather than reordering them.
Prioritise
SAGE scores the event against what it means in this environment: what the asset is worth, whether the identity is privileged, whether the behaviour departs from that entity's own baseline, and what the external context says. Severity is a property of the situation, not of the rule that fired.
Hand off
The event passes to investigation with everything already attached. Because AI SIEM, AI UEBA and AI SOAR share one data layer rather than exchanging tickets, nothing is re-fetched and no context is lost at the boundary.
What it collects, and what it writes to
A SIEM is judged first on coverage. Everything downstream is limited by what never arrived.
Sources
- Endpoint and server telemetry, including EDR alert streams
- Firewall, proxy, VPN and network flow records
- Identity provider, directory and privileged access logs
- Cloud control plane, workload and container telemetry
- SaaS application audit trails
- Database, application and custom log sources
- OT and ICS traffic through Spharaka Signal
Context attached on ingest
- Asset owner, criticality and business service
- Identity role, privilege level and peer group
- Threat intelligence reputation and attribution
- Vulnerability exposure for the specific host
- MITRE ATT&CK tactic and technique
Where output goes
- AI SOAR, for response inside the same platform
- An existing SOAR or ticketing system, where one is staying
- Compliance and audit reporting, generated from the same records
- Executive and board dashboards mapped to control frameworks
- Long-term retention that stays queryable rather than archived cold
Migration from an existing SIEM runs in phases with both platforms in parallel, not to a cutover date. The AI SIEM Migration guide sets out the order detection families should move in and why.
AI SIEM against the SIEM it replaces
The differences that matter are architectural rather than cosmetic. A legacy SIEM with a machine learning module bolted on still behaves like a legacy SIEM in every row below.
| Aspect | Traditional SIEM | Spharaka |
|---|---|---|
| When parsing happens | At query time, against whatever schema each source happened to use. Slow, and brittle when a source changes. | On ingest, into one schema. A detection survives a vendor swap because it was never written against the log format. |
| What a detection is | A rule someone wrote, which needs tuning as the estate drifts and accumulates debt nobody has time to pay down. | A judgement about behaviour, informed by a baseline that updates itself as the environment changes. |
| What arrives at the analyst | Alerts, one per rule that fired, to be correlated by hand into whatever was actually happening. | Events, already joined, already enriched, already scored against what they mean in this environment. |
| Cost of keeping data | Licensed by ingest volume, which pushes teams to drop sources and creates the blind spots that matter later. | Designed so coverage is not the thing you trade away first when the bill grows. |
| What happens next | A ticket, to a separate SOAR that re-fetches the context the SIEM already had. | Investigation continues in the same platform on the same data, with nothing lost at a handoff. |
| Unknown log sources | Dropped or shelved until someone writes a parser, which is how estates end up with undocumented gaps. | Captured and classified, so an unparsed source is a known limitation rather than an invisible one. |
Where correlation actually changes the count
A single commodity phishing compromise, as a legacy SIEM reports it and as AI SIEM reports it. The underlying telemetry is identical.
Nine hundred and forty log records across the mail gateway, the endpoint agent, the identity provider, the proxy and two file servers over eleven minutes.
Seven alerts. A mail gateway detection, two endpoint process alerts, an impossible-travel alert from the identity provider, a proxy category block, and two file access anomalies. Each lands in a different queue position, at a different severity, with no stated relationship.
All nine hundred and forty records land in one schema, so the same user, the same host and the same session are the same entity across five different vendors' log formats.
The mail delivery, the process launch, the authentication and the file access are joined by identity, host and time window into one security event with a sequence.
The sending domain resolves to a known credential-harvesting campaign. The affected user holds finance approval rights. Both facts are on the event before anyone reads it.
One event, correctly scored high, with seven previously separate alerts as its evidence rather than as its competitors for attention.
The reduction is not seven alerts to one alert. It is seven decisions to one decision, and the one decision arrives with the context that the seven would each have had to be given by hand.
What to test in a SIEM evaluation
Run these against your own telemetry. A demonstration tenant is built from data that parses cleanly, which is the one thing your estate will not provide.
Point it at your worst log source
Every estate has a source with an undocumented format that the incumbent never parsed properly. How a platform handles that source tells you more about the next three years than how it handles the clean ones.
Count events, not alerts
Ask for the same day of telemetry processed by both platforms and compare how many separate things a human would have had to look at. A platform that reports a lower alert count by suppressing more is not the same as one that reports fewer because it joined them.
Change something and see what breaks
Swap a firewall, rename a cloud account, or add a new SaaS source mid-trial. A SIEM whose detections are written against log formats will quietly stop detecting, and the trial is the cheapest place to discover that.
Ask what it costs to keep everything
Model the bill at full coverage rather than at the sources you can currently afford. Blind spots created by ingest pricing are the most common root cause in post-incident reviews, and they are a commercial decision rather than a technical one.
Frequently asked questions
What is an AI SIEM?
An AI SIEM is a Security Information and Event Management platform built on agentic AI and a cybersecurity LLM. It autonomously ingests, correlates, investigates, and prioritizes security events, replacing rule-only legacy SIEM.
How is Spharaka AI SIEM different from traditional SIEM?
Traditional SIEM relies on static rules and human triage. Spharaka AI SIEM uses AI correlation, AI investigation, and threat prioritization to deliver fewer, higher-confidence security events to analysts.
Does AI SIEM include AI SOAR capabilities?
Yes, Spharaka AI SIEM is unified with AI SOAR in a single AI SOC platform, enabling autonomous detection and response without integration overhead.
Can AI SIEM be deployed on-premise?
Yes. Spharaka AI SIEM supports cloud, on-premise, and hybrid deployment with full data sovereignty for regulated industries.
Explore related capabilities
AI SOC Platform
The unified, autonomous Security Operations Center.
AI SOAR
Playbooks, automation, and orchestration.
AI UEBA
Behavioural analytics for users and entities.
Autonomous Threat Hunting
Proactive, AI-driven threat hunting.
Cyber Threat Intelligence
Multi-source CTI for IOC enrichment.
SAGE™ AI Model
Spharaka's proprietary Cybersecurity SLM for autonomous cyber defence.
Related use cases and guides
See Spharaka AI SIEM in action
Discover how Spharaka's AI-native, autonomous cyber defence platform modernises your security operations.