Automations
This pillar addresses DevSecOps workflows that detect vulnerable code, correlate threat intelligence, generate candidate fixes, and validate remediation paths before release. Content should show how custom security automation reduces exposure windows, improves patch velocity, and brings intelligence and control into modern software delivery pipelines.
This page details a custom end-to-end DevSecOps workflow that orchestrates detection, prioritization, fix generation, and validation to reduce mean time to remediate (MTTR). It explains the multi-agent architecture for integrating SCA, SAST, and threat intelligence tools, the logic for automated patch creation and testing, and the controls required for safe, auditable deployment into CI/CD pipelines.
This workflow automates the correlation of internal Software Bill of Materials (SBOM) data with live exploit feeds, CVE databases, and dark web intelligence to prioritize critical risks. The page covers the agentic architecture for data fusion, real-time risk scoring, and automated alerting to development teams, reducing manual research overhead and focusing effort on truly exploitable vulnerabilities.
This page outlines a custom system that continuously generates and analyzes SBOMs across all builds, automatically flagging newly discovered vulnerabilities in dependencies. It explains the integration with CI/CD and artifact registries, the logic for risk assessment based on context and exploitability, and the automated creation of Jira tickets or pull request comments for developers.
This workflow automates the ingestion and deduplication of vulnerability data from scanners, commercial feeds, and open-source intelligence. The page details the architecture for normalizing and scoring findings based on internal asset criticality and external threat context, drastically reducing alert fatigue and ensuring security teams focus on high-impact issues.
This page describes a custom pre-merge security gate that uses agents to scan pull requests for vulnerable dependencies before code is merged. It covers the integration with GitHub/GitLab, the logic for blocking or warning based on severity, and the automated suggestion of secure alternative libraries, shifting security left and preventing vulnerable code from entering the main branch.
This workflow uses LLM-powered agents to analyze vulnerable code snippets, understand context, and generate syntactically correct patch candidates. The page explains the architecture for code analysis, secure pattern suggestion, integration with IDE plugins or CI systems, and the human-in-the-loop review process required for safe, autonomous fix generation.
This page details a workflow where specialized agents provide inline, context-aware security fix suggestions directly in developer IDEs or pull requests. It covers the integration with tools like GitHub Copilot, the logic for suggesting fixes for common vulnerabilities (e.g., SQLi, XSS), and the architecture for learning from accepted fixes to improve future recommendations.
This workflow targets legacy or complex codebases, using AI to identify insecure patterns (like hardcoded secrets, weak crypto) and suggest or execute bulk refactoring. The page explains the static analysis integration, the safety controls for change validation, and how this automation accelerates technical debt reduction and security hardening projects.
This page outlines an automation system that identifies vulnerable dependencies, researches compatible upgrade paths, generates upgrade branches, and runs comprehensive test suites to validate backward compatibility. It details the orchestration between package managers, CI systems, and test frameworks to de-risk dependency updates and accelerate patching.
This workflow embeds security intelligence directly into code review, where an agent analyzes PR diffs, correlates them with vulnerability databases, and posts tailored fix recommendations. The page covers the integration with GitHub/GitLab APIs, the logic for minimizing false positives, and how this reduces reviewer burden and improves fix adoption rates.
This page details a critical validation layer where AI-generated security patches are automatically tested in a sandboxed environment before being approved. It explains the architecture for dynamic application security testing (DAST), unit test generation, and regression testing to ensure the fix resolves the vulnerability without breaking functionality.
This workflow automates the scheduling, execution, and analysis of DAST scans against staging or pre-production environments. The page covers the agentic coordination for target discovery, attack simulation, result triage, and the automatic creation of tickets for discovered vulnerabilities, integrating proactive runtime testing into the SDLC.
This page describes a workflow where, after a vulnerability is patched, an automated penetration testing agent specifically targets the remediated component to verify the fix's effectiveness. It details the integration with bug bounty platforms or internal red team tools, the logic for test case generation, and the reporting of verification results.
This workflow focuses on automatically generating and executing regression test suites for applications after a security patch is applied. The page explains the use of AI to analyze code changes, infer impacted user journeys, and create test cases, ensuring that security remediation maintains application integrity and user experience.
This page outlines a system where security patches are validated not just with generic tests, but against organization-specific attack simulations modeled on real threat actor TTPs. It covers the architecture for simulation orchestration, result analysis, and providing evidence that the patch effectively mitigates the modeled attack.
This page provides a comprehensive blueprint for a fully automated, agentic pipeline from vulnerability detection to production deployment. It details the orchestration of scanning, ticketing, fix generation, validation, and deployment gates, showing how to build a closed-loop system that minimizes human intervention while enforcing security policy.
This workflow uses conversational AI agents (e.g., Slack/MS Teams bots) to notify developers of vulnerabilities, gather context, and triage issues without leaving the collaboration platform. The page covers the bot architecture, integration with security tools, and logic for routing issues to the right owner or escalating based on SLA breaches.
This page details a system that automatically creates and routes vulnerability tickets to the correct development squad or individual based on asset ownership, vulnerability severity, and team capacity. It explains the logic for prioritization, assignment, and SLA tracking, streamlining the handoff between security and engineering teams.
This page focuses on building custom security automation deeply integrated into Git-based workflows. It covers agents that act on repository events, enforce security policies via merge rules, automatically create security branches, and manage secrets, creating a native, developer-friendly security experience within existing GitOps practices.
This page explains how to implement intelligent, conditional security gates in CI/CD pipelines (e.g., Jenkins, GitLab CI, GitHub Actions) that can auto-remediate certain classes of vulnerabilities. It details the decision logic, rollback procedures, and audit trails required for safe, automated intervention in the build and deployment process.
This page details a custom workflow for financial technology firms, automating vulnerability remediation while ensuring continuous compliance with PCI-DSS, SOC2, and GLBA. It covers the architecture for integrating security scans with compliance mapping, automated evidence collection, and controlled patch deployment to meet audit requirements.
This workflow is designed for healthcare software, automating the detection and patching of vulnerabilities in PHI-handling applications under strict HIPAA change control. The page explains the architecture for secure, auditable patch generation, validation, and deployment, with integrated risk assessments for business associate agreements (BAAs).
This page outlines a multi-tenant workflow for SaaS providers to manage vulnerabilities across their codebase and customer instances. It covers agentic scanning orchestration, customer-specific risk scoring, and automated, phased patch rollouts with customer notification, balancing security speed with platform stability and customer communication.
This page describes a workflow for managing security updates in automotive ECUs and embedded systems. It details the architecture for over-the-air (OTA) update package generation, signing, distribution, and vehicle-side validation, addressing the unique constraints of resource-limited devices and safety-critical certification (ISO 21434).
This workflow enables rapid, low-risk security patching for high-availability e-commerce platforms, especially during Black Friday or holiday peaks. The page explains the use of canary deployments, traffic shadowing, and automated rollback orchestration to apply critical fixes without causing downtime or impacting customer conversion rates.
This page details a workflow that autonomously scans Kubernetes clusters across AWS EKS, Azure AKS, and GCP GKE for misconfigurations and vulnerable container images. It covers the agent architecture for cluster admission control, runtime security, and automated remediation via GitOps, enforcing consistent security posture across hybrid cloud environments.
This workflow targets AWS Lambda, Azure Functions, and Google Cloud Functions, automatically scanning code and configuration for vulnerabilities and applying security best practices. The page explains the architecture for pre-deployment analysis, automated IAM policy tightening, and library dependency patching in ephemeral serverless environments.
This page goes beyond CSPM detection to automate the remediation of cloud misconfigurations in AWS, Azure, and GCP. It details the agentic workflow for analyzing CSPM findings, generating Terraform or CloudFormation correction scripts, and executing them through approval gates, continuously enforcing secure baselines.
This workflow automatically detects vulnerable base images or libraries in a private container registry (e.g., JFrog Artifactory, Azure Container Registry), rebuilds images with patched versions, and promotes them through testing stages. The page covers the pipeline orchestration, digital signing, and update propagation to running deployments.
This page outlines a system where agents analyze IaC templates for security anti-patterns (exposed ports, permissive IAM) and automatically generate corrected, pull-request-ready code. It details the integration with version control, the logic for safe modifications, and the validation of corrected templates against policy frameworks like Checkov.
This workflow automates the continuous validation of technical security controls against frameworks like NIST 800-53, CIS Benchmarks, or PCI-DSS. The page explains how agents correlate vulnerability scan data, configuration states, and log events to assess control effectiveness and generate audit-ready compliance reports automatically.
This workflow combines software composition analysis (SCA) with license compliance checking, automatically flagging dependencies that are both vulnerable and have restrictive licenses (e.g., GPL). The page details the agentic analysis, the generation of legal risk assessments, and the suggestion of commercially safe, secure alternatives.
This page describes a workflow where AI agents monitor new CVEs, attack techniques, and regulatory changes, and then draft updates to internal security policy documents. It covers the retrieval of relevant policy sections, the drafting of revised language, and the routing through a review and approval workflow for security leadership.
This workflow automates the generation and verification of Software Bill of Materials (SBOMs) and attestations required for SLSA (Supply-chain Levels for Software Artifacts) compliance. The page details the architecture for provenance tracking, artifact signing, and policy enforcement across build pipelines to harden the software supply chain.
This proactive workflow uses ML models trained on historical vulnerabilities to scan codebases for novel, potentially vulnerable patterns that static scanners miss. The page explains the architecture for code embedding, pattern matching, and risk scoring, enabling security teams to find and fix vulnerabilities before they are exploited.
This workflow intelligently schedules the deployment of security patches based on business calendars, deployment windows, and system interdependencies. The page details the AI that models risk vs. disruption, coordinates change advisory board (CAB) approvals, and orchestrates phased rollouts to maintain service availability while reducing exposure.
This workflow continuously scans code, configs, and logs for hardcoded secrets, automatically triggers rotation in vaults (e.g., HashiCorp Vault, AWS Secrets Manager), and remediates leaks by invalidating old keys and creating new commits. The page covers the end-to-end orchestration and integration with identity management systems.
This page details a workflow for securing legacy systems that cannot be easily patched. It uses agents to analyze the system, identify network-facing vulnerabilities, and automatically deploy compensating controls like web application firewalls (WAF) rules or network segmentation policies to isolate the risk until modernization occurs.
This workflow triggers a pre-defined incident response playbook automatically when a critical, actively exploited CVE is detected in the environment. The page details the orchestration of containment steps (isolating assets, revoking credentials), evidence collection, and stakeholder notification, drastically reducing response time for widespread threats.
This page outlines an automated safety net workflow that monitors application health and performance metrics after a security hotfix deployment. If anomalies are detected, agents can automatically execute a rollback to the previous known-good version, trigger diagnostics, and notify engineers, minimizing the impact of a bad patch.
This workflow automates the discovery and fixing of vulnerabilities in mobile app codebases, including insecure data storage, broken cryptography, and API key exposure. The page covers the integration with mobile build pipelines (Fastlane, Gradle), automated patching, and the re-signing and distribution of updated app binaries to stores.
This workflow continuously tests API endpoints for vulnerabilities (OWASP API Top 10) and can automatically generate fixes for issues like broken object level authorization or injection. The page details the agents for fuzzing, analyzing OpenAPI specs, and creating code patches or configuration updates to secure API gateways.
This workflow is designed for organizations managing many WordPress sites, automatically detecting vulnerable core or plugin versions, applying patches, and validating site functionality post-update. The page explains the multi-site orchestration, compatibility testing, and rollback mechanisms to manage risk at scale.
This page details a rapid-response workflow where, upon disclosure of a zero-day, AI agents analyze the vulnerability description and available patches to generate interim mitigation scripts (e.g., WAF rules, system configuration changes). It covers the automated testing and deployment of these mitigations to buy time for official patches.
This page focuses on a meta-workflow that instruments the entire vulnerability remediation process to measure and automatically optimize MTTR. It details the tracking of each stage (detection, assignment, fix, validation), identifies bottlenecks using AI, and triggers interventions (e.g., reassignment, escalation) to accelerate overall security closure rates.
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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