Why this matters
Traditional SIEMs collect plenty then leave analysts to stitch the story by hand. AgentiXCyber AI SIEM raises signal quality with adaptive correlation and contextual enrichment so your team starts from context, not chaos. Scores reflect your environment. Trails capture every move. Reports take hours, not days.
Core capabilities
Adaptive correlation
Events across identity, endpoint, network and cloud are stitched into readable timelines. Patterns that span domains are linked so analysts see one incident, not five tickets.
Contextual enrichment
Asset data, identity risk, recent changes, geo and threat intel are pulled in at ingest. Analysts do not need to swivel between tools to collect basics.
Priority scoring
Impact and likelihood are calculated with rules you can read and tune. Scores adapt to your sector, asset criticality and recent behaviour so high noise feeds stop flooding Tier 1.
Entity and relationship graph
People, devices, keys, roles, workloads and services are modelled as entities with relationships. Investigations move by following real links, not guesswork.
Investigation trails
Queries, notes, pivots and actions are captured as you work. Explanations sit next to evidence so auditors do not chase screenshots later.
Analyst copilot
Short summaries with linked evidence and suggested next steps. Analysts can accept, edit or ignore while staying in flow.
Noise controls
Suppression and grouping keep repeat patterns tidy without hiding risk. Rules are versioned and easy to review.
Integrations
We connect to SIEM and log platforms, EDR and identity, cloud and network telemetry, mail security, ticketing and chat, data lakes and object storage. Connect what you run today, then expand once value is proven.
Example use cases
Identity anomaly with device risk
Impossible travel linked to recent MFA events and endpoint telemetry. Priority score reflects user role and asset value. Playbook proposes containment with rollback.
Suspicious mailbox rule
Rule creation tied to risky sign in and recent phishing waves. Draft user outreach is ready to send after approval.
Cloud privilege escalation
New role with broad rights seen next to unusual API calls and network egress. Suggested next steps list key rotation and watchlist update.
Ransomware early signals
EDR findings mapped to file servers and backup jobs. Intel matches lift priority and trigger a containment path.
Outcomes to measure
How we deliver
1) Connect
Agree the first incident type and the sources that matter. Set success metrics and the approval gates for sensitive steps.
2) Model
Build entities and relationships for your environment. Confirm asset criticality and identity risk inputs.
3) Tune
Adjust correlation and scoring to reflect your sector and tolerance. Keep rules readable so platform teams can review them.
4) Prove
Run a pilot in a controlled scope for four to eight weeks. Measure change against the metric we agreed. Share results with evidence.
5) Scale
Roll out to similar incident types. Review quarterly. Prune what does not help.
Deployment options
AgentiXCyber runs where your policy requires.
- Private cloud or on prem inside your boundary
- Air gapped with offline update paths
- Clear identity, network and storage boundaries
- No silent egress and simple monitoring for drift
Security and governance
Security is the product.
- Least privilege across services with minimal roles
- Segmented networks with deny by default paths
- Encryption in transit and at rest
- Keys in your KMS or HSM with rotation and clear ownership
- Immutable logs with retention that matches your policy
- Version control for rules and playbooks with approvals
Ready to see it
Pick one incident type. We will show how AI SIEM lifts signal quality and speeds investigations without adding risk.
We Work with Clients to Create Solutions that Stand the Test of Time.
Frequently Asked Questions
Will this replace our SIEM
How do we keep humans in control
Can we run on prem or air gapped
How do you reduce false positives
What about performance overhead
Do analysts need to learn a new language
How do we measure success
Which stacks do you support