Knowledge Automation Security

Cybersecurity is evolving. At the core of this evolution is Knowledge Automation Security (KAS), a new era of technology that changes how we detect and respond to network threats.

Modern cyber threats evolve rapidly, acting before humans can and rarely follow set patterns. Traditional rule-based detection systems established the foundation of cybersecurity, KAS has taken over combining real time heterogeneous data gathering with a service based architecture and adaptive detections.

By harvesting data from different types of data sources, KAS bolsters its analysis with broader context, improving the speed and accuracy of Detection and Response. This cooperative approach enables real-time identification and the assessment of anomalies, empowering security teams to address risks proactively before they escalate into breaches.

Core Features:

• Knowledge – Harvesting data at scale, with no limits of real time network insights.
• Automation – Software deployment and service orchestration, leveraged with an agentic architecture, optimizing human intervention.
• Security – Heuristic and Machine learning detection capabilities applied at different granularities from a package to a context level.

KAS helps by gaining a comprehensive view of the entire network with actionable information.

Knowledge Harvesting

From Logs to Knowledge

We are no longer collecting only logs or collecting flows of a network, we are collecting and curating knowledge. From medium to big size companies, their capacity for growth is increasing everyday, this brings an essential challenge to the table. How to analyze, store and contextualize data at scale, for multiple data sources, in different locations, with high availability and multi-tenant capabilities.

Event-Driven Architecture

This is why we propose that every new generation network detection and response needs to have a built in event-driven architecture (EDA). By doing this, we simplify complex taxonomy information into a single, unified event for easier analysis and response. For example, a user logging in, a network flow, an incident alarm etc.

To cope with the multiple sources we implement a producer/consumer architecture. Which allows us to have a flexible data pipeline able to grow as needed and keep services running with the resources available without compromise.

By converting and sending events without waiting for a reply we queue or message broker, so producers can keep working without delays. This non-blocking method gives an asynchronous capability to the system, making it faster and more resilient to eventual problems.

Producer/Consumer Pipeline

To support real-time data consumers, build a pipeline with a normalization service (to clean and standardize data) and a real-time database (to store and stream updates efficiently). This ensures fast, consistent, and reliable data delivery. All this will allow us to customize the way each pipeline behaves so resources can be allocated to each of them. We will not have the same needs for flow data as we will for a log base producer.

Once the pipeline of data gathering is complete, we are ready to gather all types of events at a scale. From network flow data (netflow,sflow), machine data(snmp,ipmi,redfish..), syslog, logs, threat intelligence and more. Handling this diversity of data sources enables a comprehensive view of the environment, supporting advanced detection and response capabilities.

This pipeline should not be finished at a processing level but should also include the needed database and backup for enabling a full solution of analysis and storage. Many modern data pipelines provide just one normalization method or database type, limiting flexibility. This rigidity complicates later use cases, forcing costly workarounds or re-engineering.

Automation

From a KAS approach, automation will be divided into three categories:

Deployment/Configuration

For a service base architecture, we need the capability of auto configuration taking into account the topology, connectivity and scale of the infrastructure

Services

Asynchronous and multithread services have to be able to run and allocate the resources when they are available and planned at a background level.

Tasks

With the same requirements as a service, they will have the capability of acting upon contextualized multi service and information hybrid tasks.

Role of Workers and Jobs

Workers, jobs and other technical elements will play a key role in an automation strategy, so configuration and services can run autonomously and gather a high availability architecture. At the same time this allows a seamlessly high service performance and ensures resilience along with an exceptional user experience.

AI Agents and Agentic Architecture

It’s as of the last year that an automation strategy has been overflown by the possibilities given through AI agents. And even with its security risks which need to be addressed, not only for information leaks, but also for vulnerability compliance, an agentic architecture is a key piece of the KAS puzzle.

Agents come in to take the role for software configuration/checks, task analysis, incident analysis and more. We propose the use of a hybrid architecture using a MCP type server which opens the possibilities to use tools that connect to our existing eda architecture and also to a local database of security and context knowledge.

Smart Speed in Cybersecurity

The needs and benefits of automation in all its forms are a must for having an accurate detection and response framework. People usually hear “automation” and think it just means faster. But in cybersecurity, we want smart speed: a system that thinks and acts on its own, but within your surveillance.

Security

Security Built In

Security isn’t just the end goal, it’s built into every part of modern network detection and response. From a full SBOM, encrypted communications, to full on-prem capabilities, with flexible options to add proprietary and transparent threat intelligence to the owners discretion.

Quantum-Resistant Protection

Designed with quantum-resistant encryption in mind, the platform detects unusual spikes caused by quantum attacks, isolates the source and blocks access to critical assets like databases, endpoints and cloud systems.

This proactive response happens within seconds, before data exfiltration or internal compromise occurs. Simultaneously, the IT team is alerted with full telemetry: what was accessed, how it behaved and where it came from, making the threat response fast, informative and effective.

Anomaly Detection via Machine Learning and Behavioral Analytics

A multi-layered AI approach enables a diversified ML approach to enhance detection accuracy and adaptability.

• Neural networks: Used for pattern recognition across vast and complex data sets.
• Self learning AI: Algorithms which continuously adapt to evolving network behaviours.
• Rule-based: Include known security policies and search based techniques which can be used to explore possible threat scenarios.
• Deep learning: Also known to be used in high end chess engines, perform top level feature extraction and anomaly detections.

When correlated together, this multi-layered process enables precise analytics of a network. By utilizing this you’re able to see the movement of bad actors within a network, along with deep insights.

Establishing Baselines and Identifying Anomalies

AI algorithms and behavioral analytics help to establish the baseline of normal network behavior. Deviations from the baseline are identified as anomalies, which serve as indicators of potential security incidents. The baseline in AI-based anomaly detection typically refers to a model of “normal” network behaviour learned from historical data. Rather than simple statistical measures like means or average, baselines are often built using techniques such as unsupervised learning or other reconstruction based models. Unsupervised learning which sits within the deep learning model learns to compress and reconstruct input data, the difference between the original data and the reconstructed data is known as a reconstruction error, which is used as an anomaly score. Depending on the type of algorithm, the system might also look at how far off a prediction is or how different the data looks compared to normal patterns, to decide if something unusual has taken place.

For example, the system might detect a login pattern that doesn’t match a user’s typical behaviour. This could be something like mapping out the network at an unusual time or accessing it from a device that’s never connected before. These patterns stand out because they deviate from the historic patterns that the model has learned as “normal behavior.” The system uses machine learning techniques to identify these anomalies, often by measuring reconstruction error or detecting behavioral drift over time. Depending on how it is preconfigured, the system can either flag these anomalies for further human review or take automated action to mitigate potential threats immediately.

Unlike signature-based detection, which relies on known threat indicators, anomaly detection can identify novel or previously unknown attack vectors by focusing on behavioral deviations.

Alerting and Incident Prioritization

To mitigate alert fatigue commonly associated with security operations centers (SOCs), the system filters and correlates events to produce high-confidence alerts. This reduces false positives and enables security analysts to prioritize investigation and response efforts on incidents of genuine concern.

The platform’s architecture supports a holistic network view, enabling correlation of distributed events that may individually appear benign but collectively indicate coordinated or multi-stage attacks.

To Conclude

Evolving Cybersecurity Landscape

The cybersecurity landscape is going through large transformations, driven by the increasing sophistication and unpredictability of modern threats. Traditional security models are no longer able to manage today’s dynamic risk environment.

Introducing Knowledge. Automation. Security. (KAS)

At the forefront of this evolution is Knowledge. Automation. Security. By combining real-time network intelligence, adaptive automation and advanced threat detection to deliver faster, more accurate and more scalable security outcomes. KAS integrates these capabilities into a cohesive platform that not only enhances visibility and response times but also reduces operational complexity and alert fatigue.

Real-Time Detection and Autonomous Response

By leveraging event-driven architectures, agentic AI and machine learning, this enables organizations to detect anomalies, respond autonomously and stay ahead of evolving attack vectors, including those posed by emerging technologies such as quantum computing. Crucially, these innovations augment rather than replace human expertise, enabling security teams to concentrate on strategic analysis and high-value decision-making.

Next-Generation Cybersecurity

As compliance requirements intensify and cyber threats continue to evolve, KAS is not merely an enhancement; it represents the next generation of cybersecurity, designed for real-time defense and future-proof protection.

KAS – Empowering Security Through Knowledge and Automation