The AI Native SIEM is a modern Headless Security Information and Event Management platform designed to collect, normalize, correlate, analyze, investigate, and report on security telemetry across enterprise IT environments. AI Native SIEM combines real-time streaming analytics, User and Entity Behavior Analytics (UEBA), automated case management, AI-driven security workflows, and long-term log retention within a unified security operations platform. Unlike traditional SIEM platforms that depend heavily on retrospective searches against stored log data, AI Native SIEM is designed around a streaming analytics architecture, where security events are normalized, enriched, correlated, and evaluated as they enter the platform, allowing suspicious activity to be identified in real-time while events are occurring rather than waiting for analysts to manually search historical logs.
AI Native SIEM extends traditional SIEM functionality by connecting related activity into behavioral cases rather than presenting security teams with large numbers of isolated alerts. Identity, endpoint, network, cloud, email, and application telemetry can be correlated to provide analysts with the evidence, timeline, context, and recommended actions required to investigate potential security incidents.
What Is AI Native SIEM?
AI Native SIEM provides a centralized security analytics and operations platform for monitoring activity across users, servers, endpoints, networks, cloud services, identity systems, security products, and business applications. The Security Information and Event Management platform is designed around several closely integrated capabilities that enable it to function as both a traditional SIEM and a compolete security operations platform capable of turning raw telemetry into actionable security investigations.:
- Real-time data ingestion and normalization
- Streaming security analytics
- User and Entity Behavior Analytics (UEBA)
- Behavioral clustering and risk analysis
- Automated security case creation
- AI-assisted investigation and triage
- Security workflow automation
- Centralized log management and retention
- Security reporting and dashboards
- API and headless SIEM access
Real-Time Streaming SIEM Architecture
A fundamental difference between AI Native SIEM and traditional SIEM architectures is the use of streaming data processing. Traditional SIEM platforms commonly ingest events, index the data, store it, and then execute queries or correlation searches against the stored information. AI Native SIEM is designed to process security telemetry as it arrives, proactively delivering results in real-time. The basic security data flow can be summarized as:
Collect → Normalize → Enrich → Analyze → Correlate → Detect → Investigate → Respond → Report
AI Native SIEM Streaming Pipes can collect and normalize logs, identity information, endpoint telemetry, cloud events, SaaS activity, network security data, and other audit information in real time. Streaming Analytics can then evaluate events while they are moving through the platform. This architecture reduces dependence on repeated retrospective searches and enables detection logic to operate directly against incoming security activity.
Streaming Pipes and Data Ingestion
AI Native SIEM provides flexible mechanisms for collecting security and audit information from both cloud-based and on-premises environments. Security information is normalized and enriched during ingestion, providing a common analytical foundation for telemetry generated by any vendor or platform. Supported ingestion methods include:
- Syslog
- Encrypted Syslog over TLS
- REST and vendor APIs
- HTTPS event collection
- Splunk-compatible HTTP Event Collector (HEC)
- Microsoft Azure Event Hub
- AWS S3
- AWS CloudTrail
- AWS CloudWatch
- Cloud-to-cloud integrations
- Local Fluency Collectors
AI Native SIEM - Collector for On-Premises Systems
Organizations are not required to deploy a local collector for every implementation. Many cloud services and devices can send information directly to the AI Native SIME through supported APIs, encrypted Syslog, HTTPS, and cloud integrations. For environments containing internal systems that cannot securely communicate directly with cloud endpoints, a Collector is provided. The Collector can operate as a local virtual system that receives events from internal resources and securely forwards compressed event data to the AI Native SIEM Cloud environment using HTTPS. The Collector can also facilitate integrations such as LDAP, NetFlow, Syslog, HEC, and other internally generated security telemetry.
Broad Security Data Integration
AI Native SIEM can consolidate telemetry from many of the technologies commonly found throughout enterprise environments, including:
- Microsoft 365
- Microsoft Defender
- Microsoft Azure
- Windows Event Logs
- Amazon Web Services
- CrowdStrike Falcon
- SentinelOne
- Proofpoint
- Okta
- Duo Security
- Google Workspace
- Cisco security products
- Palo Alto Networks firewalls
- Fortinet security products
- Check Point firewalls
- SonicWall
- Sophos
- Linux Syslog
- Endpoint security platforms
- Firewalls and network security appliances
- Identity and access management systems
- Cloud infrastructure
- SaaS applications
- IBM i iSeries AS400
- Enforcive Enterprise Security: Application Audit, File Audit, System Audit, Message Queue, History, Encryption, SQL Audit, and Read-Only
- Assure Security Event Logs
The AI Native SIEM also supports generic Syslog data sources, and new parsers can be rapidly developed for data formats not known or yet supported, including event log formats JSON, CEF, Key-Value Pair, LEEF, flat, csv and others.
Behavioral Analytics and UEBA
Traditional signature-based security detection is highly effective when the characteristics of an attack are already known. It can be less effective when an attacker uses legitimate credentials, administrative tools, or applications in an abnormal way. AI Native SEIM addresses this challenge with User and Entity Behavior Analytics (UEBA). Behavioral analytics evaluates activity in the context of users, identities, devices, systems, and historical behavior. Instead of asking only whether an individual event matches a known attack signature, the platform can evaluate whether a sequence of otherwise legitimate events represents abnormal or potentially malicious behavior.
Examples can include:
- Impossible-travel authentication activity
- Unusual login locations
- Unexpected privilege escalation
- Suspicious creation of new users
- Abnormal PowerShell activity
- Unexpected email forwarding or filtering rules
- Lateral movement between systems
- Unusual endpoint activity
- Changes in normal identity behavior
Behavioral Clustering Reduces Alert Fatigue
One of the limitations of conventional security monitoring is that a single attack can generate alerts from many independent systems. An identity provider may detect an unusual login, an endpoint platform may detect a suspicious process, and a firewall may identify unusual network activity. When these events are evaluated independently, analysts can receive multiple tickets for what is actually one security incident.
AI Native SIEM is designed to correlate related events into behavioral stories and cases. Identity normalization and behavioral clustering allow activity from multiple security technologies to be associated with the same user, device, or incident. This provides analysts with a more complete view of an attack while reducing the operational burden created by isolated alerts.
Automated Security Case Creation
When the AI Native SIEM identifies suspicious behavioral activity, it can automatically create a security case containing the information required for investigation. By preserving the evidence associated with a detection, AI Native SIEM helps analysts move from an alert to an investigation without repeatedly reconstructing the incident from separate security tools. A security incident cases can include:
- Source security events
- Related identities and systems
- Behavioral context
- Incident timeline
- Supporting evidence
- Event enrichment
- Related alerts
- MITRE ATT&CK context where applicable
- Recommended investigation or response actions
AI-Native Security Operations
A SIEM that natively integrates artificial intelligence with structured security workflows to assist with investigation, triage, analysis, reporting, and response. The objective is not simply to add a conversational interface to a SIEM. The SIEM combines AI with the security evidence, behavioral analytics, case structure, and workflows already maintained by the platform. Security teams can use AI-enabled workflows to:
- Investigate security cases
- Summarize incident evidence
- Analyze suspicious activity
- Evaluate security posture
- Review detection coverage
- Perform repetitive triage processes
- Generate management reports
- Prepare executive security summaries
- Assist with detection engineering
Automated Security Workflows
AI Native SIEM provides repeatable workflows for security investigations that analysts commonly perform manually. Examples include:
Impossible Travel Analysis
Evaluates geographically inconsistent authentication activity and assembles related identity, device, location, historical behavior, and source-event evidence into an investigation.
Business Email Compromise Detection
Monitors suspicious email forwarding and filtering-rule changes that can indicate compromised Microsoft 365 accounts or Business Email Compromise activity.
New User and Privilege Analysis
Identifies suspicious account creation and privilege changes and correlates them with surrounding authentication and administrative activity.
PowerShell Analysis
Analyzes PowerShell execution patterns that can be associated with malware, privilege escalation, persistence, or other suspicious endpoint activity.
Lateral Movement Detection
Correlates network, endpoint, authentication, and identity activity that can indicate an attacker is moving between systems after gaining initial access.
Headless SIEM Architecture
AI Native SIEM extends the beyond a conventional browser-based security console through its Headless SIEM architecture. The same security logic, investigations, evidence, and workflows can be exposed through different interfaces depending on the requirements of the user or application. This approach allows security operations to be incorporated into broader business and IT processes rather than requiring every user to operate directly within a traditional SIEM console. These interfaces can include:
- Traditional analyst user interface
- Dashboards
- Scheduled and on-demand reports
- APIs
- AI assistants and agentic clients
- Automated security workflows
Security Reporting for Analysts, Managers and Executives
AI Native SIEM is designed to transform the same underlying security evidence into reports appropriate for different audiences. An analyst may require individual source events and a detailed incident timeline, while a SOC manager may need information about case volume, detection quality, data-source health, and operational trends. Executives and CISOs generally require concise information about organizational risk, security posture, major incidents, and recommended actions.
AI Native SIEM reporting capabilities include:
- Incident investigation reports
- Security operations reports
- SIEM health reports
- Data-source health and ingestion monitoring
- MITRE ATT&CK coverage analysis
- Detection-gap analysis
- Management summaries
- CISO and executive security reports
- Scheduled reports
- On-demand reports
- Real-time dashboards
MITRE ATT&CK Detection Coverage
AI Native SIEM can associate behavioral cases and detection activity with the MITRE ATT&CK framework, helping security teams understand which attacker tactics and techniques are being observed and where detection coverage may require improvement. This can help organizations move beyond simply counting alerts by providing a more structured method for evaluating detection effectiveness and identifying security-monitoring gaps.
SIEM Health and Data-Source Monitoring
A SIEM cannot detect activity that it is no longer receiving. Broken connectors, expired API credentials, configuration changes, and interrupted log forwarding can create security blind spots without immediately attracting attention. However, AI Native SIEM provides data-source health information that can identify healthy, degraded, inactive, or failed integrations. This allows security teams to identify telemetry gaps and prioritize remediation before missing data materially affects detection and investigation capabilities.
Centralized Log Management and Retention
AI Native SIEM combines security analytics with centralized event and log retention. Current customer plans include one year of data retention, including 90 days of hot, searchable data. This helps organizations maintain historical audit information for security investigations, forensic analysis, operational review, and compliance requirements without purchasing a separate basic retention tier.
Longer historical visibility can be particularly valuable when investigating attacks that began weeks or months before the organization discovered the compromise.
Pricing for customer plans with one-year retention and 90 days hot/searchable are as follows: $7/user SMB or $6/user Core or $5/user Business, $10/server, and $2.99/GB where stored-volume pricing applies. There are no additional or hidden charges for data sources, EPS tiers, onboarding, AI capabilities, or AI-token usage.
SIEM Security Integrations
AI Native SIEM is designed to complement existing security investments, rather than require organizations to replace security product with technology from a single vendor. Telemetry from endpoint detection, firewalls, identity platforms, email security, cloud infrastructure, vulnerability management, SaaS applications, and operating systems can be centralized and correlated within the SIEM.
Our vendor-neutral approach allows AI Native SIEM to provide a broader security picture by connecting evidence that would otherwise remain isolated within individual security products.
Encrypted Data Collection
AI Native SIEM supports encrypted event transmission for organizations that need to securely forward logs from internal environments. Supported mechanisms include SYSLOG over TLS and HTTPS-based communication. A local Collector can also securely forward collected information to the organization's AI Native SIEM Cloud instance using HTTPS.
Splunk-Compatible HEC Support
AI Native SIEM supports the Splunk HTTP Event Collector (HEC) specification. Applications already designed to send events to a Splunk-compatible HEC endpoint can therefore be configured to securely forward supported application and security events to the AI Native SIEM. This can simplify migration and integration for organizations with applications that already provide native HEC output.
Pricing
AI Native SIEM uses an asset-oriented pricing model rather than relying exclusively on events-per-second or raw ingestion volume as the primary licensing metric.
AI Native SIEM currently has three primary packages:
SMB
- $7 per protected user per month
- $30 monthly minimum
- 3 GB of included storage per metered asset per month
- Standard detection rules
- Support for selected standard security data sources
- One year of retention
Core
- $6 per protected user per month
- 50-user minimum
- 3 GB of included storage per metered asset per month
- Custom detection rules
- One year of retention
Enterprise Class
- $5 per protected user per month
- 200-user minimum
- 2 GB of included storage per metered asset per month
- Custom detection rules
- Syslog feeds
- HEC feeds
- One year of retention
Servers are currently priced separately at $10 per server per month. AI Native SIEM has a stored-data rate of $2.99 per GB where volume-based metering applies. Larger enterprise configurations may require individual sizing and volume pricing not advertised, so please request a quote before making purchasing decisions.
Predictable SIEM Costs with No Gotchas
The cost of a SIEM is difficult to predict when licensing is tied directly to log ingestion volume, events per second, search activity, AI usage, or when there are multiple separately licensed platform components. AI Native SIEM pricing model does not impose separate charges for data sources, events-per-second tiers, onboarding, AI capabilities, or AI-token consumption. Instead, the primary cost model is based on protected users, servers, and data volume beyond the included allowance.
This pricing model makes SIEM budgeting easier to plan for future events, as an organization adds or removes employees, servers, security technologies, and data sources.
AI Native SIEM for Small and Midsize Organizations
SIEM technology has historically been difficult for smaller organizations to deploy because of minimum license commitments, infrastructure requirements, specialized engineering requirements, and ongoing analyst workload.
The AI Native SIEM SMB package is designed to make SIEM capabilities accessible to smaller environments while retaining core platform functionality such as streaming analytics, behavioral detection, case management, and one-year retention.
The SMB pricing model makes AI Native SIEM suitable for organizations of any size that need centralized security monitoring and audit retention, yet do not operate a large internal Security Operations Center.
AI Native SIEM for Enterprise Security Operations
For larger organizations, AI Native SIEM provides a common security analytics layer across cloud, endpoint, identity, network, email, and application environments. Enterprise use cases can include:
- Centralized enterprise security monitoring
- Identity threat detection
- Cloud security monitoring
- Endpoint security correlation
- Network threat analysis
- Business Email Compromise detection
- Privileged-user monitoring
- Security incident investigation
- Log retention and forensic analysis
- Detection engineering
- Security posture reporting
- MITRE ATT&CK coverage analysis
AI Native SIEM for Managed Security Service Providers
AI Native SIEM also supports Managed Security Service Provider (MSSP) environments. Service providers can use a common platform to monitor and manage security operations across multiple customer environments while maintaining appropriate tenant context and separation. The platform's case management, reporting, behavioral analytics, automation, and headless operating model can help MSSPs standardize security services while producing customer-specific investigations and reports.
Why Organizations Choose AI Native SIEM
AI Native SIEM is designed for organizations seeking to modernize security monitoring without relying entirely on traditional search-driven SIEM operations.
Key advantages include:
- Real-time streaming analytics that evaluates security activity as events arrive
- Behavioral analytics and UEBA for identifying abnormal user and entity activity
- Behavioral clustering that connects related events instead of treating every alert independently
- Automated case creation with evidence, context, timelines, and recommended actions
- AI-driven workflows for investigation, triage, reporting, and repetitive security operations
- Headless SIEM capabilities that expose security operations through UI, API, dashboards, reports, and AI clients
- Broad security integrations across endpoint, identity, network, cloud, email, and SaaS environments
- One year of included data retention
- 90 days of hot searchable data
- Flexible cloud and on-premises data collection
- Predictable asset-oriented licensing without separate EPS or AI-token charges
Frequently Asked Questions
What does SIEM stand for?
SIEM stands for Security Information and Event Management. SIEM platforms centralize security and audit information from multiple systems so organizations can detect threats, investigate incidents, retain logs, and report on security activity.
Is AI Native SIEM a cloud SIEM?
AI Native SIEM supports cloud-based SIEM operation while providing multiple methods for collecting information from cloud and on-premises environments. Organizations can send supported telemetry directly to AI Native SIEM or deploy a local Collector when internal systems require local event collection.
Does AI Native SIEM require an on-premises collector?
No. Many data sources can communicate directly with AI Native SIEM through APIs, cloud integrations, Syslog, HTTPS, or other supported methods. A local collector is available for environments where internal collection, LDAP, NetFlow, bandwidth management, or secure forwarding is required.
Does AI Native SIEM support Syslog?
Yes. Fluency supports Syslog and encrypted Syslog over TLS. Generic Syslog sources can also be integrated, with additional parsing developed when necessary.
Does Fluency support Splunk HEC?
Yes. Fluency supports Splunk-compatible HTTP Event Collector endpoints, allowing applications capable of sending events to Splunk HEC to forward supported telemetry to Fluency.
Does Fluency include UEBA?
Yes. Fluency incorporates User and Entity Behavior Analytics and behavioral clustering to identify suspicious activity based on identity, entity, context, historical behavior, and related security events.
Does Fluency use artificial intelligence?
Yes. Fluency incorporates AI into structured workflows for investigation, triage, analysis, reporting, and other repeatable security operations. AI works with the platform's security evidence, behavioral cases, and workflow logic rather than operating only as a standalone conversational interface.
How long does Fluency retain security logs?
Fluency's current published packages include one year of data retention, including 90 days of hot searchable data.
Can Fluency monitor Microsoft 365?
Yes. Microsoft 365 is a supported Fluency data source. Fluency also supports Microsoft Defender, Azure-related telemetry, Windows Event Logs, and other Microsoft security information through supported integrations.
Can Fluency integrate with CrowdStrike and SentinelOne?
Yes. Fluency supports integrations with both CrowdStrike Falcon and SentinelOne, allowing endpoint security telemetry to be correlated with information from identity, cloud, email, network, and other security systems.
Can Fluency help reduce SIEM alert fatigue?
Fluency is designed to correlate related security activity into behavioral cases rather than presenting every detection as an unrelated alert. This provides analysts with broader incident context and can reduce the number of independent events that require manual investigation.
What is a Headless SIEM?
A Headless SIEM separates security capabilities from a single graphical interface. Fluency can expose security investigations, evidence, reports, and workflows through traditional UI components as well as APIs, dashboards, reports, automation, and supported AI clients.
Fluency SIEM Summary
Fluency SIEM combines real-time streaming analytics, behavioral detection, UEBA, automated case management, AI-driven investigation, security workflow automation, centralized log management, and long-term retention within a unified security operations platform.
Its architecture is designed to move security teams beyond traditional alert-driven monitoring by correlating activity into evidence-backed behavioral cases and providing repeatable workflows for investigating, explaining, responding to, and reporting on security events.
For organizations seeking to improve security visibility across users, endpoints, servers, cloud services, networks, identity systems, and business applications, Fluency provides a modern SIEM architecture designed around real-time analytics, behavioral context, automation, and actionable security intelligence.
