Skip to main content

Documentation Index

Fetch the complete documentation index at: https://threatbasis.io/llms.txt

Use this file to discover all available pages before exploring further.

Real-time AI processing enables security systems to analyze events, generate insights, and trigger responses as threats unfold. Streaming architectures process continuous data flows—logs, alerts, network traffic—with LLM-powered analysis that keeps pace with security event velocity. Security operations increasingly demand real-time AI capabilities: instant alert enrichment, live investigation assistance, and automated response within SLA windows. Understanding these patterns is essential for integrating AI into your security tooling infrastructure effectively. This guide covers streaming architectures, latency optimization, and implementation patterns for real-time security AI.

​
Streaming Architecture Patterns

Streaming architectures differ significantly in their latency, throughput, and complexity characteristics. Request-response patterns offer the simplest implementation for interactive queries but introduce high latency that makes them unsuitable for real-time processing. Streaming response patterns reduce perceived latency by delivering output progressively, making them ideal for live investigation assistance where analysts benefit from seeing results as they generate. Event-driven architectures provide the lowest latency and highest throughput, making them the preferred choice for alert processing pipelines. However, they require more sophisticated infrastructure including message brokers like Apache Kafka or AWS Kinesis. Batch and micro-batch patterns offer a middle ground, achieving very high throughput for high-volume analysis while accepting medium latency—suitable for scenarios where processing can tolerate brief delays.

​
Streaming Response Patterns

Different streaming patterns suit different security applications. Token streaming delivers individual tokens as the model generates them, providing immediate feedback for live investigation assistance where analysts watch AI reasoning unfold. Chunk streaming groups tokens into logical units before transmission, reducing overhead while still enabling progressive alert analysis. Event streaming treats each output as a discrete event, fitting naturally into event-driven architectures for real-time enrichment pipelines. Many production systems use hybrid approaches, combining patterns based on the specific workflow requirements—streaming tokens for user-facing interfaces while using event streaming for backend processing.

​
Real-Time Processing

​
Event Processing Pipeline

A well-designed event processing pipeline allocates latency budgets across stages to meet overall SLA requirements. Ingestion through Kafka or Kinesis should complete within 10ms. Filtering and routing adds another 5ms as the stream processor identifies relevant events. Enrichment—adding context from caches and API calls—consumes the largest portion at up to 100ms, though aggressive caching can reduce this significantly. AI analysis represents the critical path, with LLM processing consuming up to 2 seconds depending on model size and prompt complexity. Using streaming responses allows downstream stages to begin processing before the full response completes. The final action stage—triggering webhooks or API calls—should complete within 50ms to maintain responsiveness.

​
Latency Optimization Techniques

Reducing latency in real-time AI systems requires attention to multiple factors. Prompt caching reduces time-to-first-token (TTFT) by caching system prompts and common prefixes—particularly valuable for security contexts where detailed instructions repeat across requests. Selecting appropriately-sized models trades some capability for faster inference; security triage workflows may not require frontier models. Parallel processing reduces wall-clock time by making concurrent API calls for independent analyses. Edge deployment eliminates network latency by running local inference through tools like Ollama. Speculative execution pre-computes likely analysis paths, hiding latency when predictions prove correct.

​
Streaming LLM Integration

All major LLM providers support streaming through Server-Sent Events (SSE), enabling token-by-token delivery. OpenAI, Anthropic, and Azure OpenAI all provide low-latency streaming APIs suitable for real-time security applications. Local deployment through Ollama offers the lowest latency for sensitive workloads that require on-premises processing. AWS Bedrock provides streaming with slightly higher latency but integrates well with AWS security infrastructure.

​
Streaming Implementation Considerations

Production streaming implementations must address several operational concerns. Connection management requires keep-alive configurations and reconnection logic to handle network interruptions gracefully. Error handling should implement graceful degradation—falling back to cached responses or simpler analyses when streaming fails. Backpressure management prevents overwhelming downstream systems when AI responses arrive faster than they can be processed. Configure buffering strategies and rate limiting to maintain system stability. Always set configurable timeouts; LLM calls can hang indefinitely without explicit timeout handling. Build partial response handling to extract value from incomplete outputs when connections terminate unexpectedly.

​
Security Event Processing

​
Event Types and Processing

Different event types warrant different AI processing strategies based on their volume and urgency. Critical alerts occur infrequently but demand full AI analysis within one minute—these receive priority access to LLM resources and the most capable models. High-severity alerts accept up to five minutes of latency, enabling prioritized queuing during traffic spikes. Medium alerts arrive in high volumes and can tolerate 15-minute latency, making them suitable for batch analysis that amortizes API costs. Low alerts and informational events—arriving in very high volumes—use sampling and aggregation rather than individual analysis. Raw logs at extreme volumes receive best-effort pattern detection, often using simpler models or rule-based pre-filtering before selective AI analysis.

​
Real-Time Enrichment

Effective enrichment strategies match caching policies to data characteristics. Threat intelligence from TI feeds uses TTL-based caching, refreshing at intervals that balance freshness against lookup costs. Asset context from the CMDB uses event-driven refresh, updating the cache when asset changes occur rather than on fixed schedules. User context from IAM systems benefits from session-based caching that persists throughout an investigation. Historical patterns from SIEM systems work best as pre-computed summaries rather than real-time queries. Geolocation data from GeoIP databases changes infrequently, justifying long-term caching strategies.

​
Scaling Considerations

​
Horizontal Scaling

Scaling real-time AI systems requires careful attention to each component’s characteristics. Event ingestion scales through partitioning by source, though this approach requires careful consideration of ordering guarantees when event sequence matters. AI processing scales via worker pools, but API rate limits and cost constraints often cap practical concurrency. State management through distributed caches must balance consistency requirements against latency. Eventually-consistent caches work for enrichment data but may not suit stateful analysis workflows. Output delivery uses fan-out patterns, with delivery guarantee requirements determining whether to use at-least-once or exactly-once semantics.

​
Cost Management

Managing costs in real-time AI systems requires multiple strategies working together. Tiered processing routes events by priority, reserving expensive AI analysis for high-value alerts while using simpler methods for lower-priority events. Implementing AI guardrails ensures AI resources aren’t wasted on inappropriate content. Caching common analyses prevents redundant API calls for similar events—particularly valuable for enrichment queries that repeat across related alerts. Micro-batching groups similar events to amortize per-request overhead. Right-sizing model selection balances cost against capability; not every analysis requires a frontier model.

​
Anti-Patterns to Avoid

  • Synchronous blocking — Blocking on LLM calls kills throughput. Use async processing and streaming responses to maintain system responsiveness during AI analysis.
  • Ignoring backpressure — Overwhelming downstream systems causes cascading failures. Implement flow control, buffering, and circuit breakers to handle traffic spikes gracefully.
  • No timeout handling — LLM calls can hang indefinitely without explicit timeouts. Always configure request timeouts and implement fallback behavior for timeout scenarios.
  • Over-processing — Not every event needs AI analysis. Implement intelligent routing based on event severity, type, and content to allocate AI resources efficiently.
  • Missing observability — Real-time systems need real-time monitoring. Instrument latency, throughput, error rates, and queue depths throughout the pipeline. See AI observability patterns for detailed guidance.
  • Inadequate security controls — Streaming AI systems process sensitive security data and must implement proper prompt injection defenses and output validation to prevent manipulation.

​
References