Zero-Trust Security

Zero-Trust Data Security

An Industry Brief

What is Zero-Trust Security?

Zero-Trust is a security concept and cybersecurity framework allowing an organization to aggressively defend itself, its data, and user permissions using an advanced system of automated security protocols. Zero-Trust is centered on the belief that organizations should not automatically trust anything inside or outside their perimeters and instead must verify anything and everything trying to connect to their systems before granting access. Continue Reading Zero-Trust Data Security

The Three Key Components to a Zero-Trust Model

  1. User/Application authentication – Authenticate the user or the application (in cases where applications are requesting automated access) irrefutably to ensure that the entity requesting access is indeed that entity.
  2. Device authentication – Just authenticating the user/ application is not enough.  Authentication is required for both the user and the device requesting access.
  3. Trust – Access is only granted once the user/application and the device are authenticated and authorized to access the information requested.

How Does a Zero-Trust Framework Function?

The Zero-Trust framework dictates that the organization cannot trust anything, inside or outside of its perimeters, without authentication. The Zero-Trust model operates on the principle of “never trust, always verify”, both for internal and external access. It assumes that, in a potential security breach, the concept of an internal “safe” perimeter is dead.  Zero-Trust stipulates that an organization can no longer operate with the idea of establishing “lower levels” of security within its own internal perimeter or networks, nor should an organization implicitly trust data requests coming from within an internal “trusted” perimeter. If there is a data security breach, the Zero-Trust model compensates for the fact that exploitation and spread of the attack footprint is likely to mask itself as coming from “internal” requests, previously assumed to be less of a threat. This has, unfortunately, been proven true in multiple attacks as attackers are able to breach the perimeter and request further access through trusted connections via tactics such as phishing attacks.

Traditional “internal/external” perimeter-based security models also can’t scale to meet evolving business requirements and threats or adapt to a situation where the perimeter has already been compromised. In dynamic, online environments, a Zero-Trust model provides a common-sense approach to cybersecurity that includes 5 key steps to Zero-Trust implementation.

 Implementing a Zero-Trust Approach in 5 Key Steps

Steps to Implement Zero-Trust

Step 1: Prioritize

Prioritize the investment in and roll-out of a Zero-Trust security framework based on the business realities and potential impact within the individual organization. The organization must individually weigh the gravity and likelihood of potential security threats and vulnerabilities and consider their importance to critical business operations.

Step 2: Protect

When rolling out a Zero-Trust framework, an organization must first work to identify and protect the most potentially vulnerable and strategically important systems, people, devices, and networks to preserve critical operations and limit the potential attack impact of a data breach. A Zero-Trust implementation must consider the business’ order of operations, and most aggressively protect those organizational assets that could be most serious if compromised.

Step 3: Predict

The business’ data intelligence and cybersecurity should work together to help identify potential threats or highlight vulnerabilities. An organization should strengthen its risk posture with AI-powered predictive threat prevention, using predictive analytics to understand deviations from normal usage. These anomalies and deviations from historical usage can be critical to dynamically isolating and potentially stopping the spread of an attack.

Step 4: Isolate

Using an advanced system of automated protocols, a Zero-Trust system quickly & dynamically identifies and isolates critical assets when the system detects usage anomalies. This allows for potential rogue users or data breaches to be shut down on an automated basis, pending further investigation, in minutes as opposed to hours.

Step 5: Remediate

In using machine learning and business intelligence to automate anomaly detection, organizations are able to respond within minutes to a potential threat and lessen the impact of a potential breach. Using advanced technology, an organization can minimize the operational impact of attacks by drastically reducing response time and connecting business data to the cybersecurity infrastructure, utilizing machine learning.

Elements & Processes of a Zero-Trust Network

Zero Trust requires several key technologies and processes to implement including:


Micro-segmentation is the foundation for Zero-Trust. Micro-segmentation allows administrators to program advanced security policies based on where and how a workload might be used, what kind of data it will access, and how important or potentially sensitive the data or application is.

Multi-Factor Authentication

Uses multiple types of authentication to enforce stronger authorization. For example, when you have to confirm a code that is texted or emailed to you in order to log in on a system, that is multi-factor authentication.

Identity and Access Management

Ensures that the correct users have access to only those permissions that they need to have access to. Diligent identity and access management authenticates (ideally, using multiple factors) both the user/application and the device being used and limits them to only those capabilities/permissions they need in order to perform their function.

User and Network Behavior Analytics

The use of data intelligence to understand and predict how users and networks typically behave. User and network behavior can predict thresholds for request volume and identify when escalations of access requests should be flagged as an “anomaly”. Using machine learning, data analytics can help security teams to understand the relative behaviors of users and networks, the origin of requests for access, and can highlight any unusual behavior, which may indicate a compromised identity.

Endpoint Security

Endpoint security is the establishment of security standards for the devices and clients that can access data on secured networks. Proper endpoint security ensures the endpoint device is clean, up-to-date, has any security patches installed, and will not act as a conduit for an attacker to gain unauthorized access to data.


Encryption ensures that data, while in transit, cannot be intercepted in a legible format. Encryption can be used from endpoint to endpoint in order to ensure that the data being transmitted is secure while en route. Proper use of encryption and decryption protocols prevent “sniffing” of traffic on the wire or during transmission.


The use of cybersecurity “scoring” uses various factors in order to assign a “confidence value” in a user or device requesting access being genuine and authorized to do so. Data intelligence, past usage, authentication, and access history can establish a numerical confidence ‘score’ based on the parameters above, determining whether access can be safely granted.

The Benefits of a Zero-Trust Model

Success in business today includes assuring customers of their data’s security. No one wants their organization to be the one that makes their customers’ fears come true. However, according to Forbes, 78% of employees lack confidence in their company’s cybersecurity. This is true for non-consumer-based companies as well, that need to ensure their “crown jewels” and competitive information are not stolen and ransomed.

No matter the industry, there are constituents who depend on every business to protect their data and privacy. Every organization has valuable data that nefarious actors would like to capitalize on and all business operations that hackers can interrupt causes great cost to both reputation and financial wellbeing. The pandemic has served as a wake-up call for many cybersecurity leaders on the importance of cyber threats. Innovative cybersecurity leaders are shifting away from a strategy of preventing intrusion into dealing with inevitable intrusions effectively by shrinking the attack surface and preventing data exfiltration that can leave a business in tatters.

By using a Zero-Trust model, an organization can employ a more cutting edge and data-security-centered approach to address security scope and exploitation risks.

How Sightline Systems Helps Organizations Implement Zero-Trust

Sightline Systems and Unisys have come together to develop SIAS™, providing end-to-end Zero-Trust Data Intelligence & Security by combining Sightline’s data collection and analytics with Unisys’s state of the art security platform.

Sightline SIAS



Related Content:

Histogram Data

Histogram Data

A histogram is used to summarize discrete or continuous data, grouping data points into specified range values, called “bins.” The histogram is similar to a vertical bar graph; however, the histogram shows no space between the bars. Creating a histogram provides a visual representation of data distribution, and have the ability to displace a large amount of data and the frequency of data values. The median distribution of the data can be determined by a histogram, as well as showing any outliers or gaps in the data. Continue Reading Histogram Data

MQTT Protocol

MQTT Protocol

MQTT (Message Queueing Telemetry Transport) is a simple, lightweight message publishing and subscribing network protocol. It is the standard protocol for Internet of Things (IoT) and Industry 4.0 messaging.

It is lightweight, low bandwidth, and functions well in high latency and unreliable environments, making it ideal in production environments. Devices send data (publish) to an MQTT Broker with a topic and a data payload, and devices can subscribe to that topic and subtopics and receive updates containing that data from the broker when the data changes. Topics can be defined to use several levels of depth and devices can subscribe to topics using wildcards allowing for dynamic changes when required. Continue Reading MQTT Protocol

Data Correlation

Data Correlation

The EDM Correlation data analysis tool is designed to help users perform root cause analysis across multiple devices or systems. It compares the activity of one system over a given period with the activity of multiple other systems and identifies the most highly related data points. This allows EDM to create an intuitive visualization of related events across an entire network of mixed technology enabling root cause analysis to be performed more efficiently than ever before. Continue Reading Data Correlation

Data Forecasting

Data Forecasting

Forecasting uses historical data as inputs to make informed estimates that are predictive in determining the direction of future trends. Choosing appropriate forecasting techniques depends on the type of data being used and behavior of the data. For example, forecasting techniques used for “financial data” may not be appropriate for time series data like “computer server data” or “data center network activity data” (though underlying forecasting principles stay the same). Continue Reading Data Forecasting

Colonial Pipeline-Attack-Profile

Colonial Pipeline-Attack-Profile

Sightline Cyberattack Watch: Colonial Pipeline Profile May 24, 2021 On Friday May 7, Colonial Pipeline, a privately-held company and one of the largest...