AI: The simulation of human intelligence processes by machines, especially computer systems that can reason, learn, and act in such a way that would normally require human intelligence or that involves data whose scale exceeds what humans can analyze. It includes expert systems, natural language processing (NLP), speech recognition and machine vision. It ingests large amounts of labeled training data, analyzes that data for correlations and patterns employing advanced analysis and logic-based techniques, including machine learning, deep learning, predictive modeling to interpret events, support and automate decisions, and take actions.
Alerts: A notification that can be sent to inform of system outages, changes, cyber events, attacks or emergencies. An alert is generated by a device (SIEM, firewall, DLP) based on the predefined, policies and rules you have programmed. IT alerts can be sent via cell broadcasts, email, or other communication methods. Alerts are the first line of defense against system outages or changes that can turn into major incidents, breaches or attacks. Alert monitoring minimizes risk of service degradation, outages, ransomware, and high-cost outcomes.
Anomaly Detection: A process for identifying unusual data points or patterns in a dataset. In IT, the process leverages AI, machine learning, and analytics to identify rare occurrences in network behavior patterns that may indicate a security threat or the potential for a network outage.
Adaptive Baselines: A threshold reference used for comparison that self-adjusts based on historical network behavior. Intended to control or ensure detection of suspicious activities and anomalies.
AI-Powered Analytics : Data analysis enhanced by artificial intelligence including Deep Learning, Machine Learning and Predictive Modeling. Used to identify complex patterns and relationships in data that humans might miss, leading to better predictions and insights into evasive threats in the network. It further automates efforts to diagnose what happened, predict outcomes and prescribe threat response.
Agentic Intelligence: AI systems that act autonomously, proactively making decisions, taking investigative steps, or recommending actions without explicit instructions.
Enhances real-time threat detection and response by enabling self-driven analysis, reducing manual effort and accelerating incident resolution.
Application Performance Monitoring (APM): APM focuses on tracking the health and performance of applications to ensure they meet user expectations.
Unlike APM, which focuses on the application layer, network observability provides deep visibility and the the underlying context into network paths, traffic patterns, and dependencies that impact application performance.
B
Behavioral Analytics: Analysis of patterns in user or system behavior to identify deviations that may indicate threats or issues.
Offers dynamic insights into normal vs. abnormal operations, enabling early detection of security or performance anomalies.
Business Impact Analysis (BIA): BIA assesses how IT and network events affect business operations and revenue.
Observability platforms provide contextual insights—such as which department or application is impacted—allowing IT and security teams to prioritize remediation based on business value.
C
Command and Control (C2) Traffic: Network communication between an attacker’s infrastructure and compromised systems to manage malicious activities.
Identifying C2 traffic enables real-time detection of breaches, allowing proactive mitigation and improved security posture.
Cloud-Native Monitoring: Observability tailored for cloud-based applications and services, focusing on scalability and containerized environments.
Ensures visibility into dynamic, distributed cloud systems, crucial for performance and security in modern infrastructures.
Cloud Access Security Broker (CASB): A security tool that enforces policies and monitors data and user activity in cloud applications.
Enhances visibility into cloud app usage, ensuring compliance and detecting unauthorized access or data leaks.
Cloud Security Posture Management (CSPM): A solution to identify, assess, and remediate misconfigurations or compliance issues in cloud environments.
Provides continuous insight into cloud security health, reducing vulnerabilities and improving governance.
Container Networking: The method of connecting and managing network traffic between containerized applications.
Ensures visibility into container-to-container communication, vital for performance and security in microservices architectures.
Contextual Enrichment: Enhancing raw data (e.g., flows or logs) with metadata like asset role, business function, or threat intelligence for improved analytics accuracy.
Provides deeper context to data, enabling more relevant insights and reducing false positives in security and performance monitoring.
Cloud Workload Protection Platform (CWPP) : A security solution protecting workloads (e.g., VMs, containers, serverless) across hybrid and multi-cloud environments.
Ensures visibility into workload security and performance, critical for managing dynamic cloud infrastructures.
Cloud Cost Observability : Monitoring, analyzing, and optimizing cloud usage and spending through telemetry and resource correlation.
Provides financial and resource usage insights, enabling cost-efficient cloud operations alongside performance monitoring.
D
DDoS Attacks : Known as Distributed Denial of Service, malicious network events or requests/connections intended to flood a website or network with unwanted traffic to the point that the targeted resources become overwhelmed and inaccessible. Executed from a distributed network of compromised devices formed by the attacker, the attack floods the target with requests from many different IP addresses, slowing the network and consuming capacity until websites, externally facing systems, or servers are unreachable. Cybersecurity tools like DDoS appliances, firewalls, NDR tools, and Network Observability and Defense platforms are designed to detect and defend against DDoS attacks.
Data Exfiltration: Unauthorized transfer of data from a system, often by attackers or malicious insiders.
Monitoring for data exfiltration ensures visibility into data loss incidents, enabling rapid response to protect sensitive information.
Distributed Tracing: Tracking requests as they travel through various services in a distributed system.
Provides end-to-end visibility into complex transactions, aiding in performance optimization and issue isolation.
Distributed Architecture: Distributed architecture refers to systems designed to run across multiple interconnected nodes rather than a single server.
For large enterprises, scalability in observability platforms ensures they can handle increasing traffic loads, multiple sites, and high availability without performance degradation.
E
East-West Traffic : Traffic that occurs within an internal network, such as within the boundaries of a data center or cloud. The opposite of north-south traffic that is also known as lateral network traffic, or the traffic between internal systems. The traffic includes communication between servers, workstations, or other devices and can consist of communication between different data centers within an organization. East-west traffic does not cross perimeter security devices like firewalls or gateways.
Encrypted Traffic Analysis: Inspection of encrypted network traffic to detect malicious activity without decrypting the data.
Provides visibility into secure communications, critical for identifying threats hidden in encrypted channels.
Event Correlation: Linking related events across systems to identify patterns or root causes of issues.
Enhances context-aware monitoring, enabling faster diagnosis and resolution of complex problems.
Explainable AI (XAI) : Methods and tools that make AI-driven decisions transparent and understandable, fostering trust in automated systems.
Builds confidence in AI insights, crucial for validating security alerts and performance optimizations.
eBPF (Extended Berkeley Packet Filter) : A Linux technology for safe, high-performance visibility into network, application, and infrastructure behavior without code changes.
Provides low-level, real-time insights, enhancing monitoring accuracy in modern, containerized systems.
Edge Monitoring : Observability and security for infrastructure and applications at the network edge (e.g., branch offices, IoT).
Ensures visibility into distributed edge environments, vital for performance and security in decentralized setups.
F
Flow Data: Aggregated packet data containing 5-tuple fields within a flow, and characterizing network connections or communication channel with details including source IP, destination IP, timestamp of first and last packets, the total number of bytes and packets exchanged, and a summary of the flags used in TCP connections. IPFIX/NetFlow tools like Plixer One and Scrutinizer are used to receive and analyze Flow data to monitor the network for performance and security issues.
Forensic Investigation: The process of analyzing real-time and historical network data to methodically examine evidence, and trace security incidents and performance issues to uncover details behind the event including attribution, impact, intention and root cause.
Full-Fidelity Data: Full-fidelity data refers to the collection of every network flow record without sampling. Unlike sampled data, which captures only a subset of traffic, full-fidelity data provides a complete picture of network activity.
Sampling creates blind spots that can hide security threats or performance issues. Full-fidelity visibility is essential for accurate detection, forensic investigation, and compliance reporting.
Without full-fidelity data, sampled traffic can hide anomalies, security threats, or performance issues. Observability platforms like Plixer rely on complete datasets to provide accurate detection and root cause analysis.
H
Hybrid Cloud Monitoring : The process of dually evaluating the performance, security, usage and compliance of cloud-based IT infrastructures and those implemented on-premises. With various tools that allow you to analyze, track and manage systems, devices, services and applications, organizations deployed across Cloud and datacenter environments supporting teams can proactively find and fix problems before they impact the end-user experience, data or the business.
High-Frequency Flow Sampling: Collecting network flow data at a high rate to capture detailed traffic patterns.
Provides precise, real-time insights into network behavior, essential for detecting subtle anomalies or attacks.
Hybrid Network: A hybrid network combines on-premises infrastructure with public and private cloud environments.
Observability tools must span hybrid networks to prevent blind spots, providing unified visibility across data centers, cloud providers, and remote sites.
I
IPFIX (Internet Protocol Flow Information Export): Evolving from Cisco NetFlow Protocol version 9, IPFIX is a monitoring protocol used to collect not only the general information collected by NetFlow but a broader range of data types and volumes. IPFIX formats NetFlow data and transfers the information using UDP as the transport protocol. IPFIX was first launched in early 2008 when the relevant RFCs (RFC 5101 and RFC 5102) were published as Proposed Standards by the IETF and has since become the official industry standardization of NetFlow for all flow-based monitoring protocols. Plixer leverages IPFIX in Scrutinizer and Plixer One to gather detailed network traffic information, including metadata like TCP flags, packet timestamps, and other custom fields, like application-layer data, giving it a higher degree of extensibility to enhance observability.
Incident Response : A process that involves preparing for, identifying, containing, eradicating, and recovering from a security event or an unplanned activity. The goal is to manage and reduce the potential for adverse impact, restore normal operations, and prevent future incidents.
Insider Threats: Risks posed by individuals within an organization, such as employees or contractors, who intentionally or unintentionally compromise security.
Tracking insider activities provides insights into anomalous behavior, critical for maintaining internal security and trust.
Initial Access Techniques : Methods attackers use to gain a foothold (e.g., phishing, brute force, exploiting apps) in a system or network.
Early detection of these techniques enhances visibility into breach attempts, improving proactive defense.
K
Kubernetes Observability: Monitoring and analyzing the performance, health, and security of Kubernetes clusters and workloads.
Critical for managing complex container orchestrations, ensuring reliability and detecting issues in real-time.
L
Latency: The delay in communications or network data transmission between devices, often impacting performance and user experience. It shows the time that data takes to transfer across the network. Networks with a longer delay or lag have high latency, while those with fast response times have low latency.
Logs: A collection of digital records that document events occurring on a computer network, system, application or device, providing valuable insights into system health, user actions, potential security threats, and troubleshooting information by recording details like login attempts, file access, network traffic, system errors, and configuration changes across the network and its devices. Logs essentially act as a “journal” of everything happening within a system or environment. The most common types of logs include Network Logs, Application Logs, Security Logs, System Logs and Firewall Logs.
Lateral Movement : Movement within the boundaries of a data center or cloud, between servers, workstations, or other devices. It can include movement between data centers connected and maintained by a single organization.
M
ML: This is the backbone of AI, where algorithms learn from data without being explicitly programmed. It involves training an algorithm on a data set, allowing it to improve over time and make predictions or decisions based on new data.
MTTR (Mean Time To Respond) : A key metric to building an efficient incident management process and measuring the average time it takes to address incidents. The ‘R’ is often interpreted as ‘Respond’, ‘Recovery, Repair’ or ‘Resolve’ each centered on specific tactics to drive efficiencies. At Plixer, we have centered MTTR on ‘Response’ and providing customers with the means to ensure prompt and efficient response to incidents, improving incident response procedures, and enhancing cross-functional communication.
MITRE ATT&CK Framework: A knowledge base of adversary tactics, techniques, and procedures (TTPs) based on real-world observations, used to classify and defend against cyber threats.
Provides a structured framework to detect, correlate, and respond to threats by mapping observed behaviors to known attack patterns, enhancing security observability.
Multi-Cloud Visibility: The ability to monitor and manage performance and security across multiple cloud providers.
Ensures comprehensive oversight in hybrid or multi-cloud setups, critical for unified performance and threat detection.
N
NetFlow: A network monitoring protocol developed by Cisco and widely used for collecting metadata about IP traffic flows across network devices such as switches, routers, load balancers, hosts, etc. NetFlow captures metrics about the volume and types of traffic traversing a network device. NetFlow functionality is built into network devices to enable devices to collect and export data to other systems for analysis or storage. The details of flow data captured with NetFlow include the timestamp of a flow’s first and last packets, the total number of bytes and packets exchanged, and a summary of the flags used in TCP connections. With network traffic analysis from solutions like Plixer One or Scrutinizer, NetFlow gives you deep visibility into the network and application performance without the load on the network that deep packet monitoring or active traffic monitoring causes.
Network Visibility: The next level in a network monitoring strategy which is centered on proactive awareness of everything moving through the IT network. Network visibility goes beyond monitoring data flow, device performance, and system security to ensure everything works seamlessly in the network. It is a critical IT process to discover, map, and monitor IT networks and network components, including routers, switches, servers, firewalls, and more to see across the entire digital footprint and be aware of everything in and moving through the infrastructure. Network Visibility uses a combination of network tools, each with specific purposes and limitations, to help monitor network activity, performance, traffic, data analytics, and managed resources. Visibility is often limited to data absorbed from select points on the network that offer the most visibility. Plixer One and Scrutinizer provide a 360-degree view into the network, receiving data from all network infrastructure components at the perimeter, through the data center, at the edge, and in the cloud.
Network Observability : The ability to gain deep insights into network performance, security, and behavior through telemetry, flow data, and analytics.
Network Performance Monitoring and Diagnostics (NPMD): A set of processes and tools that IT operations uses to understand and visualize the performance of applications, the network, and infrastructure components. Capabilities enable effective monitoring, analysis, and diagnosis of network performance issues and potential for service degradations related to applications and infrastructure components, as well as identifying and resolving issues affecting end-user experience and optimizing overall line-rate performance. Plixer delivers network performance management and diagnostics solutions to ensure network health, improve efficiency in diagnosing issues, conduct root cause analysis, and guarantee scalability and availability.
Network Detection and Response (NDR): A cybersecurity technology that utilizes advanced analytics, machine learning, and behavioral analysis to detect suspicious or malicious network activity. NDR tools examine network packets or traffic metadata, to identify anomalies signaling the potential for threats or attacks on the network, enabling proactive threat response. An NDR is often used as a complementary tool within a broader Security Operations Center (SOC) strategy. essentially, and goes beyond traditional signature-based detection to identify unknown threats through behavioral analysis of network traffic, artificial intelligence and machine learning. Plixer AI-driven Network Observability defense capabilities allow SOC teams to detect, investigate, and respond to hidden threats and malicious activity active in the network and stop data exfiltration and ransomware before impact to business and customers.
NetOps: An operational strategy commonly known as Network Operations that focuses on rapid deployment agility and establishing and maintaining the standard operating procedures for the digital infrastructure.
North-South Traffic : Traffic that enters and leaves the boundaries of the data center and the cloud. The opposite of east-west traffic. Commonly referred to as vertical traffic, it is communication between internal networks and external entities. It is essential for accessing external resources like websites, email, and cloud services. North-south traffic crosses the network perimeter and thus requires firewalls, VPNs, IDS or gateways to secure and control the traffic.
Natural Language Query (NLQ) : Allows users to interact with observability platforms using everyday language, bypassing complex query syntax.
Democratizes access to insights, enabling non-experts to explore data and detect issues quickly.
O
Observability: Observability is the ability to understand the internal state of a system based on the data it produces—such as logs, metrics, traces, and flow records. In networking, observability enables teams to proactively detect, diagnose, and resolve issues by providing context-rich insights into how systems are behaving across the entire infrastructure.
For Plixer, observability goes beyond simple visibility. While visibility shows what’s happening, observability explains why it’s happening. Plixer combines high-fidelity flow data with enriched context—like user activity, application performance, and threat behavior—to give network and security teams deep observability across their entire environment, from core to cloud to edge. This enables faster investigations, smarter responses, and continuous performance and security assurance.
P
Packet Capture (PCAP) : A method of intercepting and recording network data packets passing through the network for troubleshooting and forensic investigation. The data packets recorded (or captured) are downloaded, stored or analyzed to identify trends, security issues, troubleshoot networks, and more.
Plixer Replicator: A high-performance UDP packet distributed designed to serve as the single point of data distribution within a network, ingesting packet data and replicating it to any number of collectors, such as an XDR, SIEM, SOAR, Flow Collector, or analysis engine.
Plixer One: A Network Observability and Defense platform, designed to optimize visibility and security at every point in your network infrastructure. The unique platform combines network.performance monitoring with AI-powered network observability, threat detection, and response capabilities, leveraging source-enriched data from your hybrid environment. It unveils the most intricate details of hidden attacks, nefarious events, and indicators of network stress affecting service quality and reliability.
Playbook Automation: Automated workflows or scripts designed to handle specific security or operational tasks.
Ensures consistent, rapid responses to incidents, improving efficiency and visibility into resolution processes.
Prompt Engineering: Crafting effective inputs (prompts) to guide large language models (LLMs) for accurate, context-aware responses in observability or security tasks.
Optimizes AI-driven queries, improving the precision of natural language interactions with observability platforms.
Q
Quality of Experience (QoE): QoE measures the perceived performance of an application or service from the end-user perspective, including speed, reliability, and responsiveness.
Observability uses network telemetry and traffic analytics to correlate performance metrics with user experience, helping IT teams prioritize fixes that matter most to business outcomes.
R
Root Cause Analysis (RCA) : Problem analysis that goes beyond surface-level issues to uncover the reason why an event occurred. It is a systematic process of identifying the underlying causes, to find effective solutions, rather than just addressing the event or the resulting impact. It involves data collection, analysis, and identification of contributing factors with a goal of preventing recurrence of the problem.
Risk Scoring: A numerical assessment of the likelihood and impact of potential security or operational risks.
Prioritizes monitoring efforts by highlighting high-risk areas, optimizing resource allocation for threat mitigation.
RESTful APIs: Web service APIs using HTTP methods to enable communication between systems.
Facilitates integration of observability tools, enabling seamless data exchange for monitoring and analysis.
Ransomware Beaconing: Ransomware beaconing describes the periodic communication signals sent by compromised endpoints to external command-and-control (C2) servers.
Modern ransomware uses encryption, making payload detection difficult. Observability tools identify beaconing patterns in encrypted traffic without requiring decryption.
S
SecOps: The strategy that combines security and IT operations to improve an organization’s cybersecurity. Different from the SOC (security Operation centers) which serves as a team that works in isolation, it represents the holistic approach to security an organization adopts that helps security and IT operations teams work together to protect an organization effectively.
SD-WAN Visibility : The ability to glean insight into SD-WAN networks, traffic and policy enforcement or tools that deliver the same. It is the measure of your ability to understand how traffic is traversing SD-WAN paths, how applications running over the network, and in terms such as latency, packet loss, and jitter.
SASE (Secure Access Service Edge): A cloud-native security framework that converges networking and security into a unified platform intended to ensure secure, fast, and reliable connectivity for users accessing applications, and data, regardless of location.
SaaS Monitoring : Helps businesses ensure the reliability, performance, and availability of their Software as a Service (SaaS) application by providing insights into key metrics, alerting on issues, and enabling proactive management. It involves tracking key metrics, analyzing data, and managing app performance and utilization to optimize performance, address security concerns, and ensure reliability.
Smart Telemetry: Intelligent, context-aware data collection from systems to optimize monitoring efficiency.
Delivers relevant, high-quality data, reducing noise and improving accuracy in performance and security analysis.
Synthetic Monitoring: Simulating user interactions to proactively test application performance and availability.
Offers predictive visibility into user experience, enabling preemptive fixes before issues impact users.
Security Information and Event Management (SIEM): A system that aggregates and analyzes security event data for real-time threat detection and response.
Centralizes security data, enhancing visibility into incidents and improving response times.
Security Orchestration, Automation, and Response (SOAR): Tools that automate and coordinate security responses based on predefined playbooks.
Streamlines incident response, providing actionable insights and reducing manual effort in threat management.
Service-Level Agreement (SLA) Monitoring: Tracking performance metrics to ensure compliance with agreed-upon service levels.
Provides visibility into service quality, ensuring accountability and performance optimization.
Syslog: A standard protocol for sending system logs or event messages to a central server.
Centralizes log data, providing a unified view for troubleshooting and security monitoring.
SNMP (Simple Network Management Protocol): A protocol for managing devices on IP networks by exchanging monitoring data.
Offers real-time device performance insights, critical for network health and fault detection.
SPAN (Switched Port Analyzer): A feature on network switches that copies traffic to a monitoring port.
Delivers traffic visibility for analysis, supporting performance tuning and threat detection.
Service Mesh Observability : Monitoring and tracing microservice communication managed by a service mesh (e.g., Istio) for performance and security insights.
Offers detailed visibility into inter-service interactions, key for optimizing microservices-based applications.
T
Telemetry: Collected and enriched network data from various network components, applications, endpoints, logs and traces that when analyzed allow for effective network monitoring, management, and security across an IT infrastructure. It is often referred to as a technology or process that automates the measurement, collection, and transmission of data from remote, inaccessible, or distributed sources to centralized IT systems.
Throughput : The amount (or volume of data that passes through a network in a given time period. It is usually measured in bits per second (bps), megabits per second (Mbps), or gigabits per second (Gbps), and represents the actual rate at which data is transferred over the network.
Traces: A record of the flow of requests and transactions in a system to provide a comprehensive view of the system’s components and overall behavior. It provides numerical data on system performance to analyze trends and monitor the system’s current state.
Threat Intelligence : Commonly considered as data collected and analyzed on known existing and potential cyber threats. It can also refer to the process of gathering, processing and analyzing data to better understand threats and with a focus on broad trends and long-term risks, tools and tactics to carry out attacks, or timing nature or motive. Some also use the term refer any data used to enrich or provide the necessary context for the IT and security decision-making processes.
Threat Hunting: Proactive search for hidden threats within a network using manual or automated techniques beyond standard monitoring.
Enhances visibility into undetected threats, improving overall security awareness and incident prevention.
Time-Series Data: Data points collected and organized over time, often used for monitoring trends and anomalies.
Enables historical and real-time analysis of system metrics, critical for trend detection and forecasting.
TAP (Test Access Point): A hardware device that mirrors network traffic for monitoring purposes.
Provides raw traffic data, enabling deep analysis of network behavior and security.
Traffic Correlation: Traffic correlation is the process of linking data from multiple sources—such as cloud, data center, and remote offices—into a single, contextualized view.
Correlating traffic enables root cause analysis and anomaly detection across complex environments, eliminating silos between teams.
V
VPC Flow Logs: Detailed records of IP traffic entering, leaving, or within a Virtual Private Cloud (VPC) in AWS.
Provides granular network traffic insights, aiding in troubleshooting, security analysis, and compliance.
Z
Zero Trust : A cyber security model based on maintaining strict access controls. It requires continuous verification of user, device, and other factors rather than assuming trust. Key principals of the strategy require multi-factor user authentication before granting access; data protected in transit, use, and at rest; Networks segmented and monitored for unauthorized access; applications restricted to only what is essential.
Zero Trust Network Architecture (ZTNA): ZTNA is a security framework that operates on the principle of “never trust, always verify.” Every user, device, and application must be authenticated and authorized continuously.
Observability enables ZTNA by providing visibility into all traffic flows, detecting policy violations, and identifying lateral movement that could signal an insider threat.