Automation Workflow for Automated Reporting on Simulation KPIs and Resilience Metrics

This workflow automates the extraction, normalization, and aggregation of raw click-through rates, report rates, and failure patterns from multiple simulation platforms (e.g., KnowBe4, Proofpoint). It replaces a fragmented, multi-day monthly process of exporting CSVs, manual spreadsheet manipulation, and chart creation with a scheduled, serverless pipeline. The operational savings come from redeploying security analysts from report-building to strategic threat response and program tuning.

By automatically calculating metrics like Phish-Prone Percentage, Risk Score by department, and trendlines for high-risk user groups, the workflow transforms raw simulation data into a quantifiable risk posture. This enables evidence-based conversations with the board and business units, linking security awareness investment directly to reduced breach likelihood and potential financial exposure from Business Email Compromise (BEC).

The architecture includes ML-based anomaly detection that flags unexpected spikes in click rates for specific campaigns or departments in real-time. This triggers automated alerts to security leads, enabling proactive investigation—was the simulation too realistic, or is there a new, widespread threat tactic employees missed? This shifts reporting from a backward-looking audit to a forward-looking operational control.

Implementation involves a LangGraph or Prefect-orchestrated pipeline: Extraction agents pull data via platform APIs, a transformation layer normalizes and enriches it with HR data (department, location), and a calculation engine computes KPIs. Processed data is pushed to a cloud data warehouse (Snowflake, BigQuery) and surfaced in Tableau or Power BI via live connection. Governance is enforced through code, with approval gates for data blending and report publication.

The workflow generates distinct, automated dashboards for different stakeholders: a high-level risk summary for the CISO/board, a departmental drill-down for business unit leaders, and a granular campaign analysis view for security awareness managers. Every data point and calculation is backed by an immutable audit trail, crucial for compliance demonstrations (e.g., ISO 27001, SOC 2) and internal audit reviews of the simulation program's integrity.

A practical rollout starts with a 4-week pilot: ingesting data from a single simulation platform for one business unit, automating a core leadership dashboard, and validating accuracy. Phase two expands to all platforms and global subsidiaries, adding anomaly detection and integration with SIEM (e.g., Splunk) for correlating simulation failures with real security events. The final phase introduces predictive modeling to forecast risk based on training completion and historical performance.

This component orchestrates secure API calls to phishing simulation platforms (e.g., KnowBe4, Cofense), SIEMs, and HR systems to pull event logs, user metadata, and campaign parameters. It normalizes disparate schemas into a unified event model, handling data residency rules and ensuring PII-safe aggregation for global subsidiaries. Without this automated pipeline, security teams spend days each month manually exporting and reconciling CSV files from multiple tools.

Core business logic resides here. Agents calculate key risk indicators like click-through rate (CTR), report rate, and department-level risk scores. A separate ML model runs on historical trends to flag statistical anomalies—such as a sudden spike in clicks for a low-risk team—and triggers alerts for investigation. This moves reporting from descriptive to diagnostic, identifying emerging vulnerabilities before they are exploited in real attacks.

This agent uses the calculated KPIs to populate parameterized templates in tools like Power BI, Tableau, or Google Looker Studio. It generates board-ready PDFs, PowerPoint decks, and interactive dashboards, automatically segmenting data by geography, business unit, and risk tier. The system applies brand guidelines and ensures consistent narrative framing, eliminating the last-mile bottleneck of manual slide creation before quarterly reviews.

Before distribution, sensitive reports—especially those naming high-risk individuals or teams—are routed through a configured approval chain. This may involve the CISO, legal, or regional data privacy officers. The workflow manages this statefully, using a system like LangGraph to handle approvals, rejections with comments, and revisions. This embeds necessary human oversight and compliance checks into an otherwise autonomous process.

Upon approval, the workflow executes the distribution plan. This can include posting dashboards to a security portal, emailing PDF summaries to department heads, and creating tickets in ServiceNow or Jira for managers of high-risk teams to mandate remedial training. It closes the loop by ensuring insight leads to action, and can be integrated with collaboration tools like Slack for real-time alerts to security leads.

Every step of the workflow—data ingested, calculations performed, reports generated, and approvals granted—is logged to an immutable audit trail. A dedicated agent packages these logs, along with final reports, into a structured evidence bundle mapped to frameworks like NIST CSF or ISO 27001. This automates the creation of defensible documentation for internal audit and regulatory reviews, significantly reducing compliance overhead.

Key Concern: Demonstrating program ROI and workforce resilience to the board and auditors. Needs defensible metrics on risk reduction and control effectiveness.

Value from Automation: Eliminates 2-3 days of manual report compilation each month. Gains continuous, auditable dashboards showing click-rate trends, department-level risk heatmaps, and correlation of simulation failures with real security incidents. Enables data-driven arguments for budget and links training spend directly to measurable risk reduction.

Key Concern: Proving training effectiveness and efficiently targeting resources. Burdened by manual data aggregation from multiple simulation platforms (KnowBe4, Proofpoint, etc.) and spreadsheets.

Value from Automation: Automated pipeline ingests results from all platforms, normalizes data, and calculates KPIs (report rates, repeat clickers, improvement over time). Triggers alerts for anomalous failure spikes or high-risk groups. Frees up 15-20 hours monthly for strategic program design and personalized coaching, moving from data janitor to program strategist.

Key Concern: Reducing noise from false positives and integrating human risk data into threat detection. Needs to understand which user groups are most susceptible to inform monitoring rules and incident response playbooks.

Value from Automation: Automated feeds of simulation event data (e.g., via API to Splunk, Sentinel) enrich SIEM alerts with user susceptibility context. Workflow can generate watchlists for high-risk users or departments, enabling targeted logging and conditional access policies. Turns training data into an operational security control.

Key Concern: Verifying due diligence and control operation for frameworks like NIST, ISO 27001, GDPR, or FINRA. Requires consistent, tamper-evident audit trails for every simulation campaign.

Value from Automation: Autonomous agents document every step—campaign rationale, target selection, execution timing, and results—generating compliance-ready evidence packs. Maps activities directly to control IDs. Reduces pre-audit scramble from weeks to days and provides continuous assurance that the security awareness control is operating effectively.

Key Concern: Understanding team-specific risk without technical jargon. Needs actionable insights to coach their teams and protect business operations from social engineering-driven disruptions (e.g., BEC, wire fraud).

Value from Automation: Receives automated, role-tailored reports showing their team's performance against benchmarks, highlighting top phishing lures that worked. Enables managers to have data-informed security conversations. Integrates risk scores into broader operational dashboards, making security a measurable component of team performance.

Key Concern: Building a reliable, maintainable data pipeline that connects disparate systems (simulation platforms, SIEM, BI tools like Power BI/Tableau, HRIS) with appropriate governance and error handling.

Value from Automation: A clear blueprint using orchestration (e.g., LangGraph, Airflow) to extract, transform, and load (ETL) simulation data. Includes components for anomaly detection (e.g., sudden drop in report rates), exception routing for data quality issues, and secure API integrations. Provides a production-grade architecture that scales and adapts as new simulation tools are adopted.