Automations
This pillar covers security workflows that detonate suspicious artifacts, analyze their behavior, and coordinate signature or policy updates without relying solely on manual reverse engineering. Pages should show how a custom malware workflow improves zero-day response, scales analyst capacity, and integrates sandboxing, LLM reasoning, and defensive tooling into one pipeline.
This page details the core automation architecture for a custom malware analysis pipeline, where agents orchestrate sandbox detonation, behavioral analysis, and IOC extraction. We cover how to build a workflow that reduces analyst triage time, accelerates zero-day response, and integrates sandbox APIs, LLM reasoning, and threat intelligence platforms into a scalable, production-grade system.
This page explains a custom workflow that uses AI agents to score, categorize, and route incoming malware samples based on severity, campaign relevance, and organizational risk. We detail the architecture for ingesting samples from multiple sources, applying ML-based risk scoring, and prioritizing analyst workloads to reduce mean time to detection (MTTD) and focus resources on high-impact threats.
This page outlines a custom automation workflow where agents parse sandbox reports, network captures, and memory dumps to extract IOCs, enrich them with threat intelligence, and push them to security tools. We cover the integration with platforms like MISP or TIPs, the logic for deduplication and confidence scoring, and how this workflow accelerates proactive defense and blocks threats faster.
This page details a custom, high-throughput workflow for automating the execution and behavioral profiling of malware across multiple OS and application environments. We explain the architecture for parallel sandbox orchestration, feature extraction from system calls and registry changes, and automated report generation to scale analysis capacity beyond manual limits.
This page covers a custom workflow where AI agents analyze novel malware behavior to automatically generate and test candidate signatures for EDR, IPS, and antivirus systems. We detail the architecture for pattern extraction, false-positive testing, and secure deployment to defensive tools, reducing the window of exposure for previously unknown threats.
This page explains a custom workflow that automates the detection of malicious use of legitimate system tools (like PowerShell, PsExec, WMI). We cover the agentic logic for baselining normal behavior, analyzing script content and execution chains, and generating high-fidelity alerts to catch fileless and LotL attacks that bypass traditional AV.
This page details a custom automation workflow focused on ransomware, where agents detonate samples in isolated environments to extract encryption algorithms, command-and-control details, and potential decryption keys. We cover the architecture for memory forensics, network traffic analysis, and automated reporting to support incident responders and potentially enable data recovery.
This page outlines a custom workflow for automatically detonating and analyzing malicious documents in secure sandboxes. We explain the agentic orchestration for safely opening files across different software versions, monitoring for macro execution and exploit behavior, and extracting embedded payloads or IOCs to protect against common initial infection vectors.
This page covers a custom workflow where AI agents are trained to identify when malware attempts to detect and evade analysis environments. We detail the architecture for monitoring system checks (e.g., for VM artifacts, debugging tools), classifying evasion tactics, and triggering alternative analysis paths to ensure accurate behavioral capture.
This page explains a custom workflow that automates the initial assessment of security alerts, correlating them with malware analysis results to assign severity and route incidents. We cover the integration with SIEM/SOAR platforms, the logic for contextual scoring using asset criticality and threat intelligence, and how this reduces SOC analyst alert fatigue and speeds critical response.
This page details a custom, automated response workflow where, upon confirmation of a malware outbreak, agents coordinate the isolation of infected endpoints via EDR APIs and network segmentation tools. We cover the architecture for rapid IOC deployment, containment policy execution, and exception handling to minimize lateral movement and operational disruption.
This page outlines a proactive custom workflow where AI agents continuously query EDR telemetry using hypotheses generated from malware analysis findings. We explain the architecture for autonomous hunting loops, anomaly detection, and evidence collection, enabling security teams to find compromised hosts that evaded initial detection, thereby reducing dwell time.
This page covers a custom workflow that automates the detection of post-exploitation lateral movement techniques used by malware. We detail the agentic logic for analyzing authentication logs, network connections, and suspicious administrative tool usage across the environment, and triggering automated blocks or investigative actions to contain an active threat.
This page explains a custom workflow for autonomously removing malware artifacts, restoring files from backups, and validating system integrity after an incident. We cover the architecture for coordinating with EDR, backup systems, and configuration management databases (CMDBs), executing approved playbooks, and generating restoration reports to accelerate recovery and reduce manual effort.
This page details a custom workflow where agents automatically compile a forensic timeline from malware detonation logs, endpoint telemetry, and network data. We explain the architecture for time-synchronization, event correlation, and narrative generation, producing a defensible audit trail for root cause analysis, regulatory reporting, and lessons-learned sessions.
This page outlines a custom workflow where AI agents continuously evaluate, filter, and prioritize external threat intelligence feeds based on organizational relevance and malware analysis findings. We cover the logic for scoring feed quality, deduplicating IOCs, and automatically integrating high-value intelligence into security controls, improving the signal-to-noise ratio for defenders.
This page covers a custom workflow that uses AI to analyze historical malware data, emerging vulnerabilities, and adversary TTPs to forecast likely future campaigns. We detail the architecture for data fusion, model training, and generating actionable intelligence briefings, enabling proactive defense preparations and resource allocation.
This page explains a custom workflow where agents translate malware analysis findings (e.g., malicious domains, IPs, ports) into specific security policy updates for firewalls, proxies, and endpoint controls. We cover the architecture for change validation, approval gates, and safe deployment across multi-vendor environments to rapidly block malicious infrastructure.
This page details a custom workflow that automates the analysis of software components, build pipelines, and third-party libraries for signs of compromise or malicious code injection. We explain the integration with CI/CD tools, SBOM analysis, and behavioral sandboxing of artifacts to detect supply chain attacks before they reach production, protecting development and deployment environments.
This page outlines a custom workflow where AI agents plan and execute simulated attacks, using techniques and malware variants discovered in real-world analysis. We cover the architecture for safe campaign orchestration, impact assessment, and automated report generation, providing continuous security validation and helping to harden defenses against current adversary methods.
This page covers a custom, industry-specific workflow that integrates malware analysis with transaction monitoring systems to detect banking Trojans, credential stealers, and fraud. We detail the architecture for correlating endpoint compromises with anomalous financial activity, triggering fraud alerts, and automating containment to protect customer assets and meet regulatory expectations.
This page explains a custom workflow tailored for healthcare, automating the analysis of malware involved in potential PHI breaches. We cover the integration with EHR and medical device security, the logic for assessing data exfiltration risk, and automating breach notification processes to comply with HIPAA timelines and reduce manual investigation overhead.
This page details a custom workflow for operational technology environments, where agents analyze malware for ICS-specific impacts (e.g., ladder logic manipulation, PLC code). We explain the architecture for air-gapped or restricted sandboxing, safety-system aware analysis, and generating OT-focused containment playbooks to protect industrial control systems from disruptive attacks.
This page outlines a custom workflow that automates the detection of Magecart-style skimmers and other payment-focused malware on e-commerce platforms. We cover the agentic logic for scanning web assets, analyzing JavaScript obfuscation, and correlating findings with customer fraud reports to quickly identify and remediate checkout page compromises.
This page covers a custom, multi-tenant workflow enabling MSPs to automate malware analysis and alerting across their client base. We detail the architecture for secure sample handling, client-specific IOC deployment, and scalable reporting, allowing MSPs to deliver higher-value threat detection services without linearly increasing analyst headcount.
This page explains a custom workflow where AI agents automatically enrich SIEM alerts with context from malware sandbox analysis. We cover the integration patterns, the logic for pulling relevant IOCs and TTPs, and updating alert severity and ownership, dramatically reducing the time analysts spend manually investigating and correlating events.
This page details a custom workflow that deeply integrates automated malware analysis as a core capability within a SOAR platform. We explain the architecture for building custom playbooks that trigger sandbox detonation, parse results, and automate response actions, turning isolated analysis into a force multiplier for the entire security operations lifecycle.
This page outlines a custom workflow for automatically formatting and pushing structured malware analysis results (IOCs, YARA rules, TTPs) to Threat Intelligence Platforms (TIPs) like ThreatConnect or Anomali. We cover the data normalization, enrichment, and distribution logic that ensures analysis findings are immediately actionable across the security ecosystem.
This page covers a custom workflow that automates the creation, updating, and routing of tickets based on malware analysis outcomes. We detail the integration with IT service management tools, the logic for assigning tickets to SOC, IT, or risk teams, and adding analysis artifacts as attachments, streamlining collaboration and ensuring accountability for remediation.
This page explains a custom workflow for detecting and analyzing malware within cloud workloads, particularly in serverless functions and container images. We cover the architecture for automated extraction of code from cloud storage, sandboxing in cloud-native environments, and triggering CSPM policy violations or quarantine actions to secure dynamic infrastructure.
This page details a custom workflow that integrates automated malware analysis directly into the container CI/CD pipeline. We explain the agentic orchestration for scanning images at build and registry stages, analyzing binaries and layers for malicious content, and blocking infected images from deployment to production Kubernetes clusters.
This page outlines a custom workflow where suspicious email attachments and URLs are automatically extracted and sent for sandbox analysis. We cover the integration hooks with email security platforms, the logic for updating sender/domain reputation based on analysis results, and automating the release or deletion of quarantined messages to stop phishing and malware delivery.
This page covers a custom workflow that uses AI agents to perform the initial, repetitive tasks of a Tier-1 SOC analyst for malware-related alerts. We detail the architecture for alert enrichment, false-positive filtering, basic investigation, and standardized response recommendation, freeing human analysts to focus on complex threat hunting and incident response.
This page explains a custom workflow where AI agents synthesize raw sandbox logs, system changes, and network data into structured, narrative analysis reports. We cover the templates, LLM orchestration, and quality control gates that produce consistent, actionable reports for internal stakeholders, executives, or external sharing, saving analysts hours per investigation.
This page details a custom workflow that assists human reverse engineers by automating preliminary static analysis of malware binaries. We explain the architecture for disassembly, string decoding, import/export analysis, and AI-powered code annotation, highlighting suspicious functions and potential capabilities to accelerate deep-dive investigations.
This page outlines a custom workflow that automates the ingestion, hashing, clustering, and deduplication of malware samples from various sources. We cover the logic for identifying known families, storing samples efficiently, and maintaining a clean, searchable repository that maximizes the value of collected intelligence and avoids redundant analysis.
This page covers a custom workflow that automates the collection and packaging of evidence related to malware detection and response controls for audits (e.g., SOC 2, ISO 27001). We detail the architecture for querying analysis logs, pulling executed playbooks, and generating compliance reports, reducing the manual scramble during audit periods.
This page explains a custom workflow designed to detect malware that uses AI/ML techniques to evade traditional detection. We cover the architecture for analyzing samples for evidence of generative or adversarial ML components, and employing counter-AI models within the sandbox to maintain analysis integrity against this emerging threat class.
This page details a custom workflow focused on detecting malware that operates only in memory, without writing files to disk. We explain the agentic logic for deep memory analysis during sandbox execution, hunting for reflective DLL loading, PowerShell injection, and other in-memory artifacts that traditional file-scanning approaches miss.
This page outlines a custom workflow for automating the analysis of malware targeting IoT and embedded systems. We cover the architecture for emulating diverse device firmware, analyzing for modified binaries or backdoors in constrained environments, and generating risk assessments for OT and smart device security teams.
This page covers a custom workflow that automates the detonation and behavioral analysis of mobile malware in device emulators and application sandboxes. We detail the integration with mobile threat defense (MTD) platforms, the logic for simulating user interactions, and extracting mobile-specific IOCs to protect corporate BYOD and managed device fleets.
This page explains a custom workflow for automating the security analysis of smart contracts and blockchain transactions for malicious code or fraud. We cover the architecture for static and dynamic analysis in Web3-specific sandboxes, detecting reentrancy attacks, rug pulls, and other crypto-focused malware to protect digital asset operations.
This page details a custom workflow that correlates malware analysis findings with API traffic monitoring to detect data exfiltration attempts. We explain the logic for identifying malware beaconing through legitimate API endpoints, analyzing exfiltrated data patterns, and triggering automated blocks or alerts to prevent sensitive data loss.
This page outlines a custom GRC workflow where AI agents analyze internal malware incident data and external threat trends to automatically update organizational risk registers. We cover the integration with risk management platforms, the logic for scoring impact and likelihood, and generating mitigation recommendations, keeping risk assessments current and data-driven.
This page covers a custom workflow that automates the mapping of malware analysis and response activities to regulatory frameworks like NIST CSF or ISO 27001. We detail the architecture for tagging analysis processes with control IDs, generating evidence of compliance, and identifying control gaps to streamline audit preparation and demonstrate security maturity.
This page explains a custom workflow that automates the assessment of vendor security by analyzing malware samples or threats associated with their digital footprint. We cover the logic for scoring vendor risk based on threat intelligence, past incidents, and automated analysis of shared files or links, providing data for procurement and vendor management decisions.
This page details a custom workflow that uses automated malware analysis to support cyber insurance processes. We explain the architecture for generating evidence of security controls for applications, and for rapidly analyzing incidents to provide forensic details required for claims, potentially speeding payouts and demonstrating due diligence to insurers.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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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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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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