AI Workflow for Autonomous Scheduling and Deployment of Training Campaigns

Manual scheduling, list management, and deployment coordination for global campaigns consumes 15-20 hours per month for a typical security analyst. An autonomous agent integrates with HRIS (like Workday) and calendar systems to avoid conflicts (holidays, product launches), selects target cohorts based on dynamic risk scores, and executes via API to platforms like KnowBe4 or Cofense. This reclaims analyst time for higher-value threat hunting and program strategy.

When a new phishing TTP emerges in threat feeds, the window to train employees before an attack is critical. A manual process—intel review, scenario authoring, scheduling—can take 2-3 weeks. An autonomous workflow ingests structured threat intelligence (e.g., from Recorded Future), maps IOCs to scenario templates, and schedules a campaign within the next valid training window, often within 24 hours. This directly improves organizational resilience by shrinking the defender's reaction time.

Blasting simulations to the entire company on a fixed schedule leads to desensitization and poor engagement. An autonomous scheduler uses reinforcement learning to optimize send times per employee segment—avoiding Monday mornings for finance, targeting remote staff during core collaboration hours—based on historical open and click rates. This data-driven timing improves engagement and click-through rates, making the training signal more accurate and actionable.

For regulated industries (FINRA, HIPAA), proving due diligence in training requires meticulous documentation of who was tested, when, and with what content. Manual evidence collection is error-prone. An autonomous workflow, built with agents in LangGraph, logs every decision—cohort selection rationale, scheduling logic, deployment proof—and generates compliance-ready reports and audit trails automatically. This reduces the pre-audit scramble from days to hours and strengthens regulatory posture.

A blanket training approach wastes budget on low-risk employees while under-training high-risk roles (e.g., finance, executives). The autonomous workflow continuously ingests data from IAM, HR, and past simulation performance to maintain dynamic risk profiles. It allocates more frequent and sophisticated campaigns to high-risk cohorts, ensuring security spend directly targets the areas of greatest financial and reputational exposure. This improves the ROI of the entire security awareness program.

Static, predictable campaigns cause employees to become trained to the test, not the threat. The autonomous scheduler integrates with a dynamic difficulty adjustment workflow. If an employee consistently passes tests, the system automatically schedules more advanced simulations (e.g., multi-stage, deepfake vishing). This personalization prevents fatigue, maintains engagement, and provides a truer measure of resilience, leading to a stronger overall security culture.

The central decision-making agent that ingests employee risk scores from the SIEM/IAM, company calendars from HR systems, and global threat levels to autonomously generate an optimized campaign schedule. It uses rule-based logic (e.g., avoid holidays) and ML models to predict the most effective timing for each user segment, balancing training frequency with burnout risk. This agent outputs a manifest of approved campaigns to the deployment queue.

This multi-agent subsystem is triggered by the scheduler. It pulls from a library of scenario templates and enriches them with real-time threat intelligence (e.g., recent malware lures). A personalization agent then safely incorporates role-based context (e.g., finance lures for AP teams) using synthetic data or anonymized HR attributes, ensuring relevance without PII exposure. Outputs are fully rendered email HTML, landing pages, and metadata.

A controlled integration layer that manages the secure sending of simulation emails via Microsoft 365 Graph API, Exchange Online, or a dedicated SMTP service. It handles whitelisting to bypass internal filters, manages sender reputation, and ensures simulations are marked correctly to avoid confusion with real phishing. This component also integrates with collaboration tools (Slack/Teams APIs) for multi-channel attack simulations.

This system captures all employee interactions—clicks, reports, data entry—via tracking pixels, API callbacks from landing pages, and integration with email security gateways. It normalizes this data, attributing actions to individual users (for personalized feedback) while aggregating it for cohort analysis. The pipeline feeds a real-time dashboard and triggers the feedback agent.

Triggered immediately after a user interacts with a simulation. This LLM-powered agent analyzes the user's action (clicked, reported, etc.) and generates a personalized coaching message, explaining the red flags in that specific email. It can also trigger the assignment of a short, relevant training module in the LMS (via SCORM API), closing the learning loop within minutes of the simulation event.

A governance agent that runs in parallel with all campaign activities. It automatically logs every decision made by the scheduler and generator, documents the targeting rationale, and records all user interactions with timestamps. It assembles this into a defensible audit package, mapped to frameworks like NIST CSF or ISO 27001, ready for internal audit or regulatory review, eliminating manual evidence collection.