Automations
This pillar addresses carrier-grade security workflows that monitor traffic behavior, detect attacks or compromise, and coordinate containment across critical network infrastructure. The content should explain how custom anomaly-response automation strengthens national-scale telecom security, improves incident response speed, and integrates with SOC and NOC tooling.
This foundational page details a custom, end-to-end orchestration architecture for carrier-grade security, from real-time traffic behavior monitoring to coordinated containment actions. It explains how integrating detection agents, SOAR platforms, and network control APIs reduces incident dwell time and operationalizes a proactive security posture, with implementation covering data ingestion pipelines, multi-agent reasoning, and SOC/NOC handoff logic.
This page outlines a custom workflow for autonomously detecting volumetric and application-layer DDoS attacks in real-time and triggering mitigation via scrubbing centers or edge filtering. It covers the architecture for correlating flow telemetry, BGP feeds, and behavioral baselines to reduce service impact and lower reliance on manual SOC intervention during high-volume attacks.
This page describes a custom, agentic workflow where specialized AI agents collaborate to detect advanced intrusions across 5G core, RAN, and signaling layers. It details how orchestration across Snort/Suricata logs, UEBA, and threat intelligence reduces false positives and automates initial containment, improving mean time to detect (MTTD) for telecom security teams.
This page explains a custom automation workflow that continuously analyzes SS7 and Diameter signaling traffic to detect fraud, location tracking, or interception attempts. It covers the integration with signaling firewalls, the use of graph models to identify relationship-based attacks, and the automated triggering of blocking rules to protect subscriber privacy and network integrity.
This page details a custom workflow that ingests CDR and signaling data to identify and block Wangiri, IRSF, and PBX hacking fraud in real-time. It explains the architecture for pattern recognition, integration with billing systems (BSS) for revenue protection, and the automated update of blacklists or firewall policies to prevent financial loss.
This page covers a custom workflow for monitoring massive IoT device fleets, detecting compromised or malfunctioning endpoints based on traffic deviations, and automatically isolating them into quarantined network slices. It addresses the scale challenge, integration with CMP/IoT platforms, and the reduction of lateral threat movement from unsecured devices.
This page outlines a custom automation architecture that monitors HLR/HSS, registration events, and customer behavior to detect SIM swap or cloning fraud. It details how the workflow triggers step-up authentication, alerts subscribers, and automatically locks accounts, reducing account takeover losses and strengthening customer trust.
This page describes a custom SOC workflow where AI agents enrich, correlate, and score security alerts from SIEM, EDR, and network tools to triage incidents autonomously. It explains how integrating threat intelligence and business context slashes analyst review time and ensures high-severity threats are routed for immediate response.
This page details a custom workflow that, upon confirmed threat detection, automatically isolates compromised subscribers or devices into secure, monitored network slices using SDN/NFV controllers. It covers the orchestration between security analytics and network orchestration (MANO) systems to contain blast radius without taking entire services offline.
This page explains a specialized workflow where agents coordinate to identify a compromised SIM, isolate it from the network, trigger a replacement order in the BSS, and notify the subscriber—all without manual ticketing. It focuses on reducing the window of exploitation and automating the full remediation lifecycle.
This page outlines a custom workflow that translates threat intelligence and live attack patterns into specific firewall (ACL) rules, tests them in a sandbox, and deploys them across distributed enforcement points. It addresses the operational delay in manual rule management and reduces the attack surface reactively and proactively.
This page details a custom workflow built to sit atop a SIEM, where agents pull context from CMDBs, threat feeds, and past incidents to enrich raw alerts. It explains how this automation reduces alert fatigue, provides analysts with actionable intelligence upfront, and cuts down mean time to triage (MTTT) significantly.
This page describes a custom orchestration layer that ensures security incidents (SOC) and network faults (NOC) are correlated and tickets are synchronized in both ServiceNow and SIEM systems. It eliminates operational silos, prevents duplicate work, and provides a unified view of malicious vs. benign outages.
This page explains a custom workflow where reasoning agents apply business logic and entity behavior analytics to raw SIEM events, suppressing false positives and elevating true threats. It covers the architecture for integrating with Splunk, QRadar, or Sentinel to improve SOC efficiency and signal-to-noise ratio.
This page details a custom integration architecture that connects SOAR platforms like Palo Alto XSOAR or Swimlane directly to telecom network APIs (firewalls, HSS) and ticketing systems. It demonstrates how complex, multi-step incident response playbooks can be executed autonomously with human approval gates only where required.
This page outlines a custom workflow for the cloud-native 5G core, monitoring SBA interfaces (HTTP/2), NF registration, and service-based attacks. It explains the architecture for ingesting cloud telemetry, detecting credential stuffing or API abuse, and triggering scaling or isolation actions within the Kubernetes-based 5GC environment.
This page describes a custom workflow focused on the RAN edge, detecting jamming, rogue base stations (IMSI catchers), and signaling storms. It covers integration with RAN Intelligent Controllers (RIC), the use of RF fingerprinting, and automated countermeasures like power adjustment or frequency hopping to maintain service integrity.
This page details a custom security automation workflow for virtualized network functions, monitoring for VM escape, resource exhaustion, and east-west traffic anomalies. It explains integration with cloud security posture management (CSPM) and orchestrators (OpenStack, Kubernetes) to harden the NFVI layer autonomously.
This page outlines a custom workflow for protecting 5G Network Exposure Function (NEF) and northbound APIs from abuse, scraping, and credential-based attacks. It covers automated API traffic baselining, anomaly detection, and the dynamic revocation of compromised API keys or throttling of malicious partners.
This page explains a custom workflow that monitors access logs to subscriber databases (CRM, BSS) and detects anomalous query patterns indicative of a data exfiltration or insider threat. It details automated alerting to DPOs, session termination, and compliance reporting for regulations like GDPR in telecom.
This page describes a custom workflow that applies ML models to metadata (JA3, TLS headers, flow patterns) of encrypted traffic to identify malware C2, data leakage, or shadow IT. It addresses the blind spot of encryption without decryption, automating the hunting process and generating high-fidelity leads for analysts.
This page details a custom DLP workflow for telecoms, where agents scan outbound traffic, cloud storage, and API calls for unencrypted CPNI or PII. It explains automated policy enforcement—blocking, quarantining, or redacting—and integration with data classification engines to prevent sensitive data loss.
This page outlines a custom, real-time workflow that analyzes inbound roaming records (TAP files) to detect subscription fraud, inflated traffic, or gateway attacks. It covers integration with steering of roaming platforms to automatically block fraudulent partners and trigger financial reconciliation processes.
This page describes a custom automation platform where AI hunters are tasked with proactively searching logs, netflow, and asset inventories for IOCs and behavioral TTPs. It details how this shifts the SOC from reactive to proactive, uncovering hidden compromises and reducing dwell time through automated hypothesis generation and investigation.
This page explains a custom workflow that continuously compares firewall rules, system configurations, and access policies against CIS benchmarks or internal gold standards. It automates the detection of drift, generates remediation tickets, and can push corrective configurations, ensuring continuous compliance and a hardened posture.
This page details a custom DFIR workflow where agents coordinate evidence collection from endpoints, servers, and logs, perform timeline analysis, and draft initial incident reports. It dramatically reduces the manual effort in post-breach investigations, preserving evidence integrity and accelerating root cause analysis.
This page outlines a custom workflow that correlates disparate security events across the telecom infrastructure to identify and track coordinated attack campaigns. Using graph analytics and threat intelligence, it automates the mapping of attacker TTPs, infrastructure, and goals, providing strategic intelligence for defense prioritization.
This page describes a custom workflow that aggregates evidence from security tools, generates compliance reports, and maps controls to frameworks like NIST CSF or ISO 27001. It automates the tedious compilation process for audits, reducing preparation time and providing continuous assurance to regulators and leadership.
This page details a custom workflow that operationalizes security frameworks by continuously testing technical controls (e.g., access reviews, patch levels) and generating exceptions. It integrates with GRC platforms, automatically validating that security policies are enforced and highlighting gaps for remediation.
This page explains a custom workflow where agents collect, normalize, and hash log data from diverse network and security systems to create immutable, consolidated audit trails. It automates integrity checks and alerting on log tampering, which is critical for forensic investigations and regulatory compliance in telecom.
This page outlines a custom workflow that automates the assessment of telecom vendors and partners by ingesting external threat feeds, vulnerability scans, and compliance certificates. It generates dynamic risk scores, triggers re-assessments, and integrates with procurement systems to enforce security requirements during onboarding.
This page details a custom workflow for monitoring the network access and activity of third-party suppliers (e.g., tower maintenance, software vendors). It uses UEBA and network micro-segmentation to detect anomalous vendor behavior and can automatically revoke VPN or direct access privileges upon policy violation.
This page describes a custom workflow that ingests SBOMs from network equipment vendors, correlates components with known vulnerabilities (CVEs), and assesses patch criticality. It automates the risk assessment of firmware and software in routers, switches, and optical gear, prioritizing updates for the most critical assets.
This page explains a custom workflow that uses behavioral analysis and cryptographic hash verification to detect unauthorized firmware modifications on routers, switches, and base stations. It automates alerting and can initiate device isolation or rollback to a known-good image, protecting against supply chain attacks.
This page outlines a custom workflow that detects security issues affecting end-users (e.g., account compromise, phishing sites mimicking the carrier) and automatically sends personalized, actionable alerts via SMS or the carrier app. It improves customer trust and reduces support call volume by providing direct, automated guidance.
This page details a custom workflow that scans the web, email feeds, and SMS gateways for phishing campaigns impersonating the telecom brand. Upon detection, it automates takedown requests to hosting providers, blocks malicious domains at the network level, and warns subscribed customers en masse.
This page describes a custom workflow for protecting customer self-care portals and APIs from credential stuffing, scraping, and fraud bots. It uses behavioral biometrics and traffic analysis to distinguish humans from bots, automatically challenging or blocking malicious traffic to protect user accounts and backend systems.
This page explains a custom workflow that analyzes RAN telemetry and subscriber device reports to detect the presence of unauthorized cell sites (Stingrays). It automates the geolocation of these devices, alerts security teams, and can coordinate with regulators or law enforcement for physical takedown.
This page outlines a custom workflow that monitors login patterns, device fingerprints, and transaction behavior on customer mobile apps to detect ATO attempts in real-time. It triggers step-up authentication, session termination, and automated customer notification, reducing fraud losses and improving digital service security.
This page details a custom workflow where AI agents are tasked with safely probing the telecom network for vulnerabilities, simulating attacker TTPs, and generating prioritized findings reports. This automates continuous security validation, supplementing annual manual tests and providing constant assurance of defensive posture.
This page describes a custom workflow that uses threat intelligence to generate realistic attack simulation scenarios (e.g., ransomware in the BSS, DDoS on signaling) and executes them in a controlled manner. It automates the testing of detection and response capabilities, providing measurable metrics for SOC improvement.
This page explains a custom workflow that injects security-focused failures (e.g., killing a critical IDS sensor, simulating a compromised admin account) into the production-like environment. It automates the observation of system response, identifying single points of failure and validating that security controls fail securely.
This page outlines a custom workflow that integrates scanner outputs (Tenable, Qualys) with asset criticality and threat intelligence to automatically prioritize vulnerabilities for remediation. It generates patching tickets in ServiceNow/Jira, slashing the time security teams spend on manual triage and risk scoring.
This page details a custom workflow that ingests planned network changes (from orchestration systems) and simulates the potential new attack surfaces they create. It automates risk assessments for new 5G slices, API exposures, or partner interconnects, providing security-by-design feedback before deployment.
This page describes a custom workflow designed to identify attacks that use AI to evade traditional defenses, such as polymorphic malware or AI-generated phishing. It uses adversarial ML techniques to detect anomalies in attack patterns and automates countermeasures like model retraining or heuristic rule updates.
This page outlines a custom workflow for securing satellite communication links used for backhaul or remote coverage. Agents monitor for signal jamming, spoofing, and unauthorized access, automating mitigation through frequency hopping or link failover to terrestrial networks, ensuring service continuity.
This page explains a custom security automation workflow for the disaggregated O-RAN environment, focusing on the RIC, xApps, and open fronthaul interfaces. It details monitoring for malicious xApps, policy violations, and inter-component attacks, with automated responses coordinated through the RIC and SMO.
This page details a custom workflow that ingests geopolitical intelligence feeds, correlates them with the telecom's physical and logical assets in specific regions, and automatically triggers defensive actions. This could include blocking IP ranges from hostile states, increasing monitoring on critical infrastructure, or simulating attacks to test preparedness.
This page describes a custom workflow that, during and after an incident, automatically quantifies financial impact (revenue loss, regulatory fines, remediation costs) by pulling data from BSS and operational systems. It provides real-time dashboards for leadership and suggests cost-optimized response actions to minimize total loss.
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.
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