Malicious cells of dangerous trends can be found sometimes in small information amount and sometimes only after processing time series volumes. Our platform finds them on the spot.
Process terabytes of security data across multiple formats and sources simultaneously
10TB+ per hour
Analyze multiple data dimensions including time, geography, threat type, and severity
1000+ dimensions
Enterprise-grade infrastructure supporting organizations across all continents
99.9% uptime SLA
Scalable Processing is the core capability that lets every signal Andeavour collects, whether from an autonomous AI agent inside AgentGuard, an external reconnaissance attempt surfaced by AgentHunter, or a shipment record inspected by Anforcer Antifraud, get analyzed at the moment it matters rather than in a nightly batch job. Attackers, and autonomous AI systems acting on their behalf, do not wait for your ingestion pipeline to catch up, so any security platform that throttles under load effectively creates a detection blind spot precisely when adversaries are most active.
Enterprises we work with typically operate a mix of legacy SIEM exports, cloud-native audit logs, API gateway traffic, and, increasingly, telemetry generated by AI agents making thousands of autonomous tool calls per hour. Each of these sources has a different shape, velocity, and retention requirement. Scalable Processing normalizes and correlates all of it on a single timeline so that the platform can reason about cause and effect across systems that were never designed to talk to each other.
Distributed computing architecture enables simultaneous processing of multiple data streams with automatic load balancing and failover capabilities.
Cloud-native storage solutions that automatically scale based on data volume and processing requirements with intelligent data lifecycle management.
Intelligent scaling algorithms that adjust processing capacity in real-time based on workload demands and data complexity patterns.
Data enters through streaming collectors deployed as sidecars, API webhooks, or log forwarders, and is immediately partitioned by entity, whether that entity is a human identity, a service account, an AI agent, or a shipment ID. Each partition is processed by an independently scaling worker pool, so a spike in agent tool-call volume in one business unit never starves detection capacity for another.
From there, events are enriched with context, identity metadata, geolocation, historical baselines, and asset criticality, before being handed to the correlation engines described in our methodology. Enrichment and correlation both run in-memory against a rolling window, which is what keeps end-to-end alert latency under 100ms even as raw ingest volume grows into the terabytes-per-hour range.
Most detection platforms are validated against demo-scale data sets and then quietly drop events, sample down, or queue processing once real production volume arrives. That degradation is rarely visible on a dashboard; it shows up months later as a missed incident that, in hindsight, had a detectable signal buried in a backlog.
Scalable Processing is built so that ingestion, enrichment, and correlation capacity scale independently of each other and of your existing SIEM. This is also what makes the Agentic SOC practical for organizations running thousands of concurrent AI agents: every agent action is treated as a first-class event rather than sampled telemetry.
Daily security data processing for small to medium businesses
Large enterprise security operations with multiple business units
Multi-national corporations and government agencies
Transaction volume triples during quarter-end reconciliation, and hundreds of AI agents spin up to reconcile ledgers simultaneously. Scalable Processing auto-scales ingestion capacity for that window without a change request, keeping AgentGuard detection latency flat.
A marketplace platform onboards 50,000 new third-party integrations in a single quarter. Elastic storage and parallel enrichment absorb the new API surface without a corresponding increase in mean-time-to-detect for credential abuse.
A supply chain audit ingests years of shipment and customs records in a single batch for historical fraud analysis by Anforcer Antifraud, completing in hours rather than weeks because the processing layer scales horizontally on demand.
No. Collectors attach to existing log forwarders, SIEM exports, and API gateways, and the processing layer scales independently behind that ingestion point, so there is no change to how your systems emit data.
Accuracy is unaffected by volume because enrichment and correlation scale horizontally rather than sampling events. Every event that enters the pipeline is scored, not a subset of it.
It is the foundation underneath Agentic SOC, AgentGuard, and Anforcer, and is described in the context of the full platform on the how the platform works page. For background on why this matters for AI agents specifically, see what is agentic AI security.
Whether you're processing gigabytes or petabytes of security data, our platform scales automatically to meet your needs without compromising on speed or accuracy.
Agentic telemetry does not arrive at human pace. A single governed AI Principal can emit more decision events in an hour than a mid-sized workforce generates in a week, and enterprises deploying agents across finance, engineering and support routinely see event volumes grow an order of magnitude within two quarters. Scalable processing is what keeps detection, policy evaluation and evidence generation constant-time while that growth happens.
The platform treats every agent action as a structured event with an identity, an intent, a target and a context. Throughput is achieved by resolving those four properties as early as possible so that later stages operate on normalized, deduplicated records rather than raw logs.
Model gateways, cloud audit trails, API gateways, identity providers and SaaS activity feeds are normalized into a single agent-action schema at the edge of the pipeline, so downstream cost does not scale with the number of source formats.
Each action is attributed to the AI Principal that created or commands the acting agent. Attribution at ingest is what makes blast-radius questions answerable later without reprocessing history.
Behavioral baselines, peer-group comparison, data sensitivity and policy scope are evaluated concurrently rather than in sequence, keeping per-event latency flat as enrichment sources are added.
Allow, escalate or block verdicts are emitted with a decision record, then written to tiered retention so recent evidence stays instantly queryable while long-horizon data remains available for audit.
Capacity is not an abstract number; it is the difference between governing agents during a peak and switching controls off to survive one. Three patterns recur.
Global retail during peak trading: order, refund and pricing agents multiply transaction-adjacent decisions across time zones for a fixed window. Controls that throttle under load push teams to disable enforcement precisely when fraud pressure is highest.
Multinational banks rolling out agents by division: volume grows stepwise as each division onboards. Pipelines sized for the first division fail at the third unless enrichment is horizontally partitioned by Principal.
SaaS platforms absorbing vendor-injected AI: when upstream vendors add agentic features, event volume rises without any internal deployment decision. Elasticity here is not an optimization; it is the only way to stay in control of a change you did not initiate.
Scale claims should be expressed as operational guarantees rather than peak benchmarks. The measures that matter to a SOC are constant-time detection, complete attribution, and evidence that remains queryable long after the event.
Time from agent action to verdict remains stable as event volume grows, because enrichment is parallel and partitioned by Principal rather than queued globally.
Sampling is acceptable for observability and unacceptable for security. Every action that touches a policy-scoped resource is evaluated in full.
Percentage of actions resolved to a named Principal and owner — the single metric that determines whether an investigation takes minutes or days.
Audit questions spanning months return in interactive time, so evidence production is a query rather than a project.
Because normalization happens once at ingest, marginal cost is driven by governed agents rather than by the number of connected log sources.
Under extreme burst the pipeline defers non-critical enrichment before it defers policy evaluation, so enforcement is the last thing to lose fidelity — never the first.
Scalable processing is the substrate beneath the products, not a standalone feature. AgentGuard depends on it to evaluate policy on every agent action rather than a sample. Agentic SOC depends on it to correlate across Principals during an investigation. Anforcer depends on it to hold behavioral baselines across supplier and invoice populations large enough to be statistically meaningful.
Because attribution and normalization occur once, adding a product or a data source does not force a re-architecture — the same event stream serves detection, enforcement and evidence.
Processing is horizontally partitioned by AI Principal, so capacity is added by scaling partitions rather than by tuning a single pipeline. Practical limits are governed by connected source capacity, not by the platform's own architecture.
Cost tracks governed agents and retained evidence rather than raw log lines, because normalization and deduplication happen at ingest before any enrichment is billed.
Retention is tiered and configurable to your audit and regulatory obligations, with recent decision records held in interactive storage and longer horizons in queryable archive.
Non-critical enrichment is deferred first; policy evaluation and decision records are preserved. Enforcement fidelity is protected ahead of analytical depth.
Yes. Pipelines can be pinned to a region so that agent telemetry and decision records never leave the jurisdiction where they were generated.
Ingestion uses existing gateways, identity providers and cloud audit trails, so most environments start producing attributed events without deploying agents on endpoints.