Automations
This pillar covers training workflows that generate realistic phishing, spear-phishing, and deepfake scenarios from current threat intelligence to harden employees continuously. Content should show how a custom simulation workflow improves security awareness program quality, reduces manual scenario authoring, and creates measurable resilience gains across the workforce.
This foundational workflow automates the end-to-end creation of realistic phishing and social engineering training scenarios, ingesting threat intelligence to generate emails, landing pages, and deepfake content. It reduces manual scenario authoring from days to minutes, improving program agility and measurable workforce resilience. The architecture combines multi-agent orchestration, content generation APIs, and integration with security awareness platforms for continuous, adaptive testing.
This workflow automatically ingests feeds from threat intel platforms, dark web monitors, and security advisories to generate timely, relevant phishing simulations. It eliminates the lag between emerging threats and training deployment, ensuring employees are tested against the latest attacker TTPs. Implementation involves parsing structured and unstructured intel, mapping to scenario templates, and triggering campaign generation within security awareness tools.
This workflow uses specialized agents to gather and synthesize open-source intelligence (OSINT) on target employees, crafting hyper-personalized spear-phishing lures. It automates the labor-intensive research and drafting process, enabling realistic testing of high-value targets like executives and finance staff. The architecture involves data collection agents, PII-safe synthesis logic, and strict governance controls to ensure ethical simulation boundaries.
This workflow automates the generation of synthetic media for vishing and deepfake social engineering tests, creating convincing audio and video impersonations of executives or trusted contacts. It addresses the growing threat of AI-powered impersonation attacks by providing realistic training material. The build involves voice cloning models, video synthesis pipelines, and secure, ephemeral hosting to prevent misuse of generated content.
This workflow automatically analyzes employee interaction with phishing simulations (clicks, reports, etc.) and generates personalized feedback, explainer videos, and micro-training modules. It closes the training loop instantly, converting failure into a learning moment without manual intervention from security teams. The system uses LLM reasoning to tailor messaging and integrates with learning management systems for delivery.
This workflow autonomously aggregates phishing simulation results, calculates key risk indicators (click rates, report rates), and generates board-ready reports and dashboards. It eliminates days of manual data wrangling each month, providing continuous visibility into program effectiveness and workforce risk posture. Implementation involves data pipeline orchestration from simulation platforms to BI tools, with automated anomaly detection and trend analysis.
This workflow intelligently schedules and launches phishing campaigns based on employee risk scores, training calendars, and avoidance of conflict periods (e.g., holidays, product launches). It optimizes training impact and reduces administrative overhead by automating the entire campaign lifecycle. The architecture uses rule-based and ML-driven scheduling agents integrated with HR systems and email platforms.
This workflow creates coordinated social engineering attacks across email, SMS (smishing), and social media platforms, simulating modern multi-vector campaigns. It tests employee vigilance across all communication channels they use, providing a more comprehensive resilience assessment. Build requires orchestration across different delivery APIs, consistent narrative generation, and tracking of cross-channel user interactions.
This workflow generates synthetic, but statistically realistic, employee personas (including job roles, interests, and communication patterns) for use in spear-phishing simulations without using real PII. It enables safe, scalable testing of hyper-targeted attacks while preserving privacy. The system uses generative AI and organizational data models, with strict governance to ensure personas cannot be reverse-engineered to real individuals.
This workflow automates the creation of detailed, defensible audit trails for every phishing simulation, documenting rationale, targeting criteria, and results for regulatory and internal audit requirements. It reduces compliance overhead and ensures program integrity. Agents work together to log decisions, generate evidence packages, and map activities to control frameworks like NIST or ISO 27001.
This workflow automatically adjusts the sophistication of phishing lures based on individual employee performance history, creating a personalized training difficulty curve. It optimizes learning by challenging users appropriately, preventing desensitization or frustration. The system uses a reinforcement learning loop that analyzes user responses and modifies future scenario attributes like sender spoofing, language complexity, and urgency cues.
This workflow automatically identifies and manages false positives—legitimate emails or simulations incorrectly flagged by email security tools—and updates whitelists to ensure training emails reach their targets. It preserves the integrity of simulation campaigns and reduces IT support tickets. Implementation involves analyzing email gateway logs, correlating with simulation data, and executing API calls to security appliances.
This workflow orchestrates complex, multi-stage attack simulations that mimic advanced persistent threat (APT) tactics, such as reconnaissance, initial compromise, and lateral phishing. It provides red-team-level testing at scale to identify process and technical control gaps. The architecture coordinates sequential agent actions across weeks, simulating realistic attacker patience and escalation.
This workflow automatically feeds phishing simulation event data (clicks, reports) into the organization's SIEM and security analytics platform. It enriches threat detection by providing context on user susceptibility and helps correlate simulation failures with real security incidents. The build requires secure API integrations, data normalization, and mapping to common event schema like CEF or OCSF.
This workflow generates phishing scenarios that are culturally, linguistically, and regionally appropriate for a global workforce, accounting for local holidays, payment methods, and business norms. It ensures training relevance for international subsidiaries, improving engagement and effectiveness. The system uses localization agents, regional threat intelligence, and cultural context databases to adapt base templates.
This workflow automates the generation of phishing simulations that test the security awareness of third-party vendors and contractors with access to your systems. It helps quantify supply chain risk and can be offered as a service to critical partners. Implementation involves secure, segmented campaign management, vendor onboarding workflows, and role-based reporting.
This workflow continuously analyzes simulation results, HR data, and role-based access to automatically classify employees into dynamic risk tiers (e.g., high, medium, low). It enables targeted resource allocation and personalized training paths without manual analysis. The system employs ML models for classification and integrates findings directly into IAM and security awareness platforms.
This workflow monitors simulation performance in real-time and autonomously adapts follow-up tactics within a campaign, such as switching from a gift card lure to a fake software update if the first fails. It mimics agile attackers, providing a more challenging and realistic test environment. Build requires a stateful orchestration layer that can conditionally branch scenario execution based on live user interaction data.
This workflow directly parses dark web forums, leak sites, and threat actor communications to extract actual phishing lures, kits, and narratives, then sanitizes and redeploys them as internal training simulations. It provides the most authentic training possible by using the attackers' own tools. The architecture involves secure scraping agents, content sanitization pipelines, and integration with simulation platforms.
This industry-specific workflow automates the generation of highly credible Business Email Compromise (BEC) and wire fraud simulations tailored to banking and finance roles, processes, and compliance jargon. It targets the sector's highest-impact attack vector, reducing financial fraud risk. The build incorporates financial transaction templates, regulatory language, and integration with mock payment systems for realistic testing.
This workflow generates HIPAA-themed phishing lures that mimic common healthcare communications (patient referrals, insurance updates, PHI requests) to test clinical and administrative staff. It addresses the sector's unique data privacy risks and compliance pressures. Implementation uses healthcare-specific terminology and integrates with EHR access log data (in a privacy-preserving way) to enrich scenario targeting.
This workflow creates sophisticated deepfake audio and video simulations for law firms, mimicking partner voices or client instructions to manipulate wire transfers or access sensitive case data. It tests resilience against the high-stakes impersonation attacks targeting legal professionals. The architecture combines voice cloning, video synthesis, and scenario logic based on common legal workflows like trust account transfers.
This workflow automates the creation of phishing scenarios that mimic supplier communications, shipping notices, and technical document requests to test manufacturing and engineering staff. It aims to protect intellectual property and prevent supply chain disruption caused by credential theft. Scenarios are built using industry-specific document formats and integrated with operational technology (OT) network models where applicable.
This workflow generates high-volume, seasonal phishing lures common in retail, such as fake gift card promotions, order confirmation scams, and loyalty account takeovers. It protects customer-facing staff and corporate functions from attacks that directly impact revenue and brand trust. The system can sync with real promotional calendars and inventory data to create highly credible lures.
This workflow creates simulations that target software developers and IT staff with lures for fake software library updates, CI/CD pipeline alerts, and code repository access requests. It addresses the software supply chain attacks prevalent in the tech industry. Implementation involves generating credible technical documentation, fake login pages for developer tools, and integration with SSO platforms.
This high-stakes workflow automates the creation and execution of personalized social engineering tests for an organization's senior leadership, focusing on board-level fraud, executive impersonation, and sensitive data extraction. It provides measurable assurance for the most targeted individuals. The architecture includes strict oversight controls, direct reporting to the CISO, and use of OSINT for hyper-realistic personalization.
This workflow is specifically designed to test the dispersed remote workforce, simulating attacks that exploit home networks, personal devices, and collaboration tools like Slack or Teams. It identifies risks inherent in hybrid work models. The system orchestrates simulations across multiple personal and corporate communication channels, accounting for varied working hours and locations.
This workflow generates advanced, multi-vector attacks specifically designed to test the vigilance of the internal IT and security teams themselves, who are prime targets for attackers seeking network access. It helps close the 'insider's blind spot' in security posture. Scenarios involve technical lures, fake security alerts, and attempts to bypass internal controls, requiring sophisticated deception and careful oversight.
This workflow automates the generation of targeted BEC simulations for accounts payable and finance teams, mimicking vendor payment change requests, fake invoice attachments, and executive payment directives. It directly targets the business process most likely to result in financial loss. The build integrates with mock ERP or accounting system interfaces to create end-to-end process testing.
This workflow creates simulations that target Human Resources personnel with lures for fake employee data requests, W2 form submissions, and benefits enrollment phishing—common attacks seeking PII. It protects the custodians of sensitive employee data. Scenarios are built using authentic HR forms and terminology, and can be timed to real payroll cycles for maximum effect.
This workflow ingests MITRE ATT&CK frameworks and geopolitical intelligence to generate simulations that replicate the specific tools, techniques, and procedures (TTPs) of named advanced threat groups. It provides state-of-the-art threat-informed defense testing. The system maps threat group behaviors to phishing lures and multi-stage scenarios, providing detailed after-action reports aligned with the ATT&CK matrix.
This workflow automatically monitors for public data breaches and generates credential-stuffing and password-reset phishing simulations that leverage the newly exposed data, testing employee response to timely, credible-seeming attacks. It turns external events into immediate training opportunities. Implementation involves ingesting breach disclosure feeds, correlating with internal email domains, and generating personalized lures within hours.
This workflow automates the creation, execution, and reporting of phishing simulations designed to meet specific regulatory requirements for financial services, healthcare, or data privacy regimes. It reduces the manual burden of proving due diligence to auditors. The system encodes regulatory rules into scenario parameters and generates compliance-ready evidence packs automatically.
This workflow generates a wide spectrum of phishing emails (from simple to highly evasive) to proactively test the detection efficacy of new email security products, configurations, or DMARC/DKIM/SPF policies before they govern production traffic. It provides quantitative security control validation. The system sends test emails through the actual mail flow, analyzes block/allow decisions, and produces granular performance reports.
This advanced workflow orchestrates a single social engineering narrative across email, voice call (vishing), and SMS (smishing) channels in a coordinated sequence, simulating modern blended attacks. It tests organizational response to complex, cross-channel manipulation. Implementation requires synchronized timing across different communication APIs, shared state management between agents, and unified tracking of the target's cross-channel interactions.
This workflow automates the setup of simulated watering hole attacks and malicious social media profiles/posts to train employees on threats outside the email inbox. It addresses the growing risk of compromise via LinkedIn, X, and industry forums. The build involves generating fake social media content, setting up controlled landing pages, and safely directing employees to these simulated threat environments for training.
This workflow centrally manages and automates the deployment of localized phishing campaigns across dozens of international subsidiaries, respecting regional regulations, languages, and IT infrastructures. It provides centralized oversight with decentralized execution, scaling global security awareness programs. The architecture features a hub-and-spoke agent model with local compliance checks and data residency controls.
This workflow uses generative adversarial networks (GANs) and other AI techniques to automatically create phishing email variants designed to evade specific machine-learning-based email security filters. It provides an offensive security testing capability for defensive ML models. The system iteratively generates content, tests it against a target filter (in a sandbox), and refines its approach to find blind spots.
This workflow generates entire phishing simulations using synthetic employee data and communication patterns, enabling realistic training in environments with strict data privacy laws (like GDPR) where using real employee data for profiling is prohibited. It allows effective training without privacy risk. The system uses differential privacy and generative models to create statistically accurate but non-identifiable synthetic datasets for scenario personalization.
This workflow integrates with network detection and response (NDR) or endpoint detection and response (EDR) tools to trigger relevant phishing simulations when specific threat indicators (IOCs) or suspicious behaviors are detected internally. It creates contextual, just-in-time training that reinforces incident response procedures. The architecture listens to security telemetry, maps IOCs to scenario templates, and launches targeted campaigns.
This workflow automates the deployment of deceptive elements (canary tokens, honeypot credentials) within phishing simulations to identify employees who not only click but also attempt to interact further (e.g., enter data on a fake page). It provides deeper behavioral insight and can feed intelligence to threat hunting teams. Agents manage the lifecycle of deception assets and correlate interaction data with user identities.
This workflow automatically discovers, downloads, and reverse-engineers real phishing kits from the internet, then safely repurposes their templates and logic for internal training simulations. It keeps training content authentic and up-to-date with criminal tooling. The system involves sandboxed execution, code analysis, and sanitization pipelines to ensure safe use of malicious artifacts.
This workflow securely correlates phishing simulation performance data with other employee performance and risk systems (e.g., HRIS, access review tools) to provide managers with a holistic view of human risk. It helps integrate security culture into business operations. The build requires careful data anonymization and aggregation, with dashboards designed for people managers rather than security analysts.
This workflow automatically generates short, engaging training modules (videos, quizzes, interactive scenarios) based on trending phishing themes and organizational failure patterns identified in simulations. It creates a continuous, adaptive content stream that keeps training fresh and relevant. The system uses simulation analytics to identify knowledge gaps and LLM/video generation tools to produce tailored micro-learning assets.
This workflow automates the scoring, ranking, and reward distribution for a gamified phishing awareness program, managing leaderboards, team competitions, and incentive payouts. It increases employee engagement and participation rates in security training. The architecture includes real-time scoring engines, integration with collaboration tools (Slack/Teams) for announcements, and APIs for reward platforms.
This workflow generates phishing simulations that use QR codes (quishing) delivered via email or print, and other mobile-centric lures, to test employee vigilance on smartphones and tablets. It addresses the shift towards mobile-first attacks and hybrid work. Implementation involves generating QR codes that lead to controlled training sites and simulating mobile-specific attack vectors like fake app notifications.
This workflow simulates Business Email Compromise (BEC) attacks that impersonate trusted vendors or partners, sending fake invoices or contract updates to accounts payable and procurement teams. It tests processes for verifying external communications, a critical supply chain defense. The system can mimic the communication style of real vendors (with permission) and integrate with procurement software for end-to-end process testing.
This workflow-centric build provides a suite of autonomous agents that interact exclusively via APIs with existing security awareness, email, and HR platforms to deploy and manage simulations. It enables enterprises to add advanced automation to their current tech stack without a rip-and-replace. The architecture is built on a flexible orchestration layer like LangGraph, with connectors for major SaaS platforms.
This workflow is specifically designed to operate within a strict Zero-Trust Network Access (ZTNA) environment, where traditional email delivery and user tracking can be challenging. It uses approved integration points with identity providers and secure access service edges (SASE) to deliver and monitor simulations without violating security policies. The build respects the principle of least privilege in all its operations.
This workflow deploys and scales its phishing simulation agents and infrastructure as ephemeral, serverless functions within a major cloud provider (AWS, Azure, GCP). It reduces operational overhead and provides massive scalability for global campaigns. The architecture uses cloud-native services for agent orchestration, data processing, and integration, ensuring cost-efficiency and resilience.
This workflow automates the generation and deployment of phishing simulations within enterprise collaboration platforms like Microsoft Teams, Slack, or Workplace from Meta. It tests employee response to malicious links, fake file shares, and impersonator accounts in these high-trust environments. Implementation uses official bot APIs and webhook integrations to create realistic, in-channel malicious interactions for training purposes.
This workflow uses phishing simulations as a proactive control to test and measure the security posture of SaaS application usage, such as identifying users who reuse corporate credentials on simulated fake SaaS login pages. It provides actionable data for SSPM platforms and identity governance. The system simulates common SaaS login pages and correlates credential entry attempts with known corporate passwords (using hashed, non-reversible techniques).
This workflow automates the calculation of return on investment (ROI) for a phishing simulation program by modeling reduced breach likelihood, lower insurance premiums, and saved manual hours against program costs. It generates financial reports to justify security awareness spending to the board. The system ingests simulation data, industry benchmarks, and internal cost structures to produce defensible financial models.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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