Technology

Embedded System

Embedded System Jonathan Poland

An embedded system is a specialized computer designed to perform a specific task. It consists of both hardware and software components that are tailored to the needs of the particular application it serves. Embedded systems are prevalent in many everyday devices, and they outnumber general-purpose computers such as laptops. The following are examples of an embedded system.

Vehicles

Vehicles may include multiple embedded systems each with its own hardware and software. For example, a system might monitor engine performance while another system might monitor safety to produce warnings.

Toys

Toys with advanced features such as a talking doll are implemented with embedded systems.

Electronics

Electronics big and small such as a washing machine or game controller.

Buildings

Components of buildings and interior design such as an automatic door.

Infrastructure

Infrastructure such as a canal gate that can be remotely controlled by an operator.

Power Systems

A solar panel system that transmits performance information to a management server.

Supply Chain

Elements of a supply chain such as a temperature-controlled container that records temperatures to guarantee that a cold shipment was never exposed to a high temperature.

Industrial

Factory equipment such as a robot.

Networking

Network devices such as a wifi router.

Cities

Smart city infrastructure such as solar lighting that self-manages its power capacity.

Theme Parks

A car on a roller coaster that monitors vibrations and seat belts that can slow and stop the attraction if it finds a problem.

Fashion

In theory, fashion could use computers to implement features such as shoes that change colors and patterns on command.

Medicine

Computers in medical devices such as a heart monitor.

Progress Trap

Progress Trap Jonathan Poland
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A progress trap is a situation where a new technology, which has the potential to improve life, ends up causing harm due to a lack of risk management. This is often due to the fact that the benefits of new technology are often emphasized and pursued, while the potential risks are overlooked or underestimated. The rapid adoption of new technologies can lead to a situation where risks are not adequately addressed, resulting in negative consequences. For example, the introduction of a new technology may create new forms of pollution or increase the vulnerability of a system to disasters. If these risks are not properly managed, they can have serious impacts on society and the environment.

To avoid progress traps, it is important to carefully assess the risks and benefits of new technologies and put in place appropriate measures to manage them. This may involve regulation, education, or other forms of intervention. By taking a proactive approach to risk management, societies can avoid the negative consequences of new technologies and reap the full benefits of progress.

Precautionary Principle

The precautionary principle is a guideline that dictates that new technologies must be proven to be safe before they can be released to the public or the environment. This principle is based on the idea that it is better to be cautious and prevent harm from occurring rather than waiting to react after the fact. According to the precautionary principle, if there is a possibility that a new technology may do more harm than good, it should not be introduced at all. This principle is often applied in the context of environmental protection, where the potential impacts on human health and the environment must be carefully considered before new technologies are introduced. The precautionary principle is an important tool for risk management and can help to prevent harm from occurring due to the introduction of new technologies. By requiring that new technologies be proved safe before release, the precautionary principle helps to protect people and the environment from unnecessary harm.

Unstructured Data

Unstructured Data Jonathan Poland

Unstructured data refers to information that is not organized in a specific, predefined way that is easily understood by computers. In the past, most computer systems required data to be highly structured in order to be processed, often using methods like tables, rows, and fields with specific data types. However, real-world information is often more complex and does not fit neatly into these structures. As a result, modern information technologies such as artificial intelligence are able to process unstructured data, which is more common in the real world.

The following are examples.

  • Writing: Textual analysis of written works such as books and blogs.
  • Social Media: Scanning streams of social media to detect real time information such as rumors about a stock.
  • Natural Language: Systems that accept voice commands or understand what people are saying for purposes such as analytics.
  • Photographs & Video: Analysis of video to understand events such as a video camera that monitors water levels flowing into a dam reservoir.
  • Communications: Scanning communications such as emails to detect spam.
  • Science: Looking for patterns in interstellar radio messages in order to discover intelligent life.
  • Health: Analysis of x-ray images for signs of disease.
  • Search: A search engine that spiders unstructured web pages in order to understand their content.

Data Proliferation

Data Proliferation Jonathan Poland

Data proliferation refers to the rapid growth of data, often resulting in a large amount of replicated and low-quality data. This can be costly to manage and may pose compliance and operational risks to an organization. While it may be necessary to analyze this data in order to understand its structure, sources, and uses, it may ultimately have little value to the organization and can be difficult to discard. The following are illustrative examples of data proliferation.

Customer Data

It is common for multiple systems in an organization to maintain customer data. Such data is commonly out of sync between systems with no clear single source of truth. This can cause operational failures such as sending a bill to the wrong address.

Documents

Knowledge workers tend to create a lot of documents that get checked into a document management system. In many cases, such documents become completely unused with time but are retained as a precaution.

Communication

Communications such as emails can gather at the rate of hundreds per employee per day. Most communications lose their value almost immediately but often are retained for an extended period of time.

Backups

Backups of data, documents and communications often need to be retained in case something important was deleted from the source systems. If someone deletes a critical email, the only copy may be in a backup from a particular day last year. As such, backups are commonly stored for long periods of time. This can consume considerable resources despite the fact that backups are rarely used.

Transactional Data

Transactional data such as market trades and website purchases can grow extremely quickly. Transactional data is often viewed as valuable for historical research. For example, it is common to look at patterns in stock trades going back decades.

Social Data

Data that is shared by people on a public or private social network. Often viewed as valuable for purposes such as market research and machine learning.

Sensors & Machines

Machine and sensor generated data. Sensors have become cheap to the extent than they can be embedded in everyday objects in great numbers. Such data may be generally less valuable than human generated data. For example, video of a train tunnel or data from a tire pressure sensor isn’t interesting for long. Nevertheless, sensor data potentially represents a gigantic source of data that is far larger than all other sources combined.

What is Big Data?

What is Big Data? Jonathan Poland

Big data refers to extremely large and complex datasets that are difficult to process using traditional data processing tools. These datasets often come from a variety of sources, such as social media, sensors, and transactional data, and can contain both structured and unstructured data. The volume, variety, and velocity of big data can make it challenging to extract value from it. However, with the right tools and techniques, organizations can use big data to gain insights and make better decisions.

One key aspect of big data is the ability to process and analyze it in real-time, also known as stream processing. This enables organizations to quickly respond to changing conditions and make decisions based on the most up-to-date information. Big data can be used in a variety of industries, including healthcare, finance, and retail. For example, in healthcare, big data can be used to improve patient care by analyzing patient data to identify trends and patterns that can help predict future health issues. In finance, big data can be used to identify fraudulent activity and improve risk management. In retail, big data can be used to optimize pricing and inventory management.

There are several tools and technologies available for processing and analyzing big data, including Hadoop, Spark, and NoSQL databases. These tools enable organizations to store, process, and analyze large volumes of data quickly and efficiently. Basically, big data refers to extremely large and complex datasets that can be challenging to process using traditional tools. By using the right tools and techniques, organizations can extract valuable insights from big data and use it to make better decisions.

Qualitative Data

Qualitative Data Jonathan Poland

Qualitative data refers to information that is expressed in a language such as English and cannot be easily quantified or measured. It is often contrasted with quantitative data, which can be represented numerically and is more easily processed by machines. While qualitative data may provide deeper insights and context, it is more difficult to analyze and requires advanced techniques such as artificial intelligence for natural language processing. Currently, most qualitative data is generated by humans, but it is possible that machines may eventually be able to express complex ideas with words as well.

The following are common examples of qualitative data.

  • Strategy – A company executive expresses a company’s strategy in words.
  • Designs – An architect describes a concept for a building to a client.
  • Communication – A stock analyst advises an investor on a private call.
  • Stories – A firm builds an extremely valuable brand by telling stories that people find compelling.
  • Comments & Feedback – A new game is released and people who try it post their feelings and ideas about it.
  • Guides – A guide describes how to write code in a particular programming language.
  • Knowledge- A physicist explains a new theory with a short analogy. The theory goes on to change our understanding of the universe and has countless applications for engineering and technology.

Data Security

Data Security Jonathan Poland

Data security is the practice of protecting data from unauthorized access, use, modification, destruction, or deletion. It is a key aspect of information security that is focused on protecting data stores, knowledge repositories, and documents from external threats and vulnerabilities.

There are several key aspects of data security that are important to understand. These include:

  1. Data protection: Data protection refers to the measures that are implemented to protect data from unauthorized access, use, or modification. This may include measures such as encryption, access controls, and authentication protocols.
  2. Data retention: Data retention refers to the practice of storing data for a specific period of time in order to meet legal, regulatory, or business requirements. Data retention policies can help to ensure that important data is not deleted or lost.
  3. Data backup: Data backup is the process of creating copies of data in order to protect against data loss due to hardware failure, software bugs, or other unforeseen events.
  4. Data recovery: Data recovery is the process of restoring data that has been lost, damaged, or corrupted. Data recovery is often necessary after a data breach or other security incident.

The following are common data security techniques and considerations.

  • Data Anonymization
  • Data Authentication
  • Data Backup
  • Data Breach
  • Data Control
  • Data In Rest
  • Data In Transit
  • Data In Use
  • Data Masking
  • Data Purging
  • Data Remanence
  • Data Room
  • Data Sovereignty
  • Data States
  • Data Subject
  • Data Wipe
  • Deep Magic
  • Degaussing
  • Self-Destruct Mechanism

Cyber Security

Cyber Security Jonathan Poland

Cybersecurity is the practice of protecting computing resources from unauthorized access, use, modification, misdirection, or disruption. It is a critical concern for individuals and organizations in today’s digital world, as the increasing reliance on technology has made it easier for cybercriminals to gain access to sensitive data and systems.

There are several key aspects of cybersecurity that are important to understand. These include:

  1. Threats: Cybersecurity threats are actions or events that have the potential to compromise the security of a computer or network. Examples of cybersecurity threats include malware, viruses, phishing attacks, and denial of service attacks.
  2. Vulnerabilities: Cybersecurity vulnerabilities are weaknesses in a computer or network that can be exploited by attackers. These vulnerabilities can include software vulnerabilities, configuration errors, and lack of security controls.
  3. Controls: Cybersecurity controls are measures that are implemented to prevent or mitigate cybersecurity threats and vulnerabilities. Examples of cybersecurity controls include firewalls, antivirus software, access controls, and encryption.
  4. Incidents: Cybersecurity incidents are events that compromise the security of a computer or network. These incidents can include data breaches, malware infections, and unauthorized access to systems.

Overall, cybersecurity is a critical concern for individuals and organizations in today’s digital world. By understanding the threats, vulnerabilities, and controls that are involved in cybersecurity, individuals and organizations can take steps to protect themselves and their computing resources from unauthorized access, use, modification, misdirection, or disruption.

Some examples of cybersecurity include: 

  1. Firewalls: Firewalls are network security systems that monitor and control incoming and outgoing network traffic based on predetermined security rules.
  2. Antivirus software: Antivirus software is designed to detect and prevent the execution of malware on a computer or network.
  3. Access controls: Access controls are security measures that are implemented to allow or deny access to specific resources or systems based on predetermined criteria.
  4. Encryption: Encryption is the process of encoding data in such a way that it can only be accessed by authorized parties with the necessary decryption key.
  5. Two-factor authentication: Two-factor authentication is a security process that requires users to provide two forms of authentication, such as a password and a fingerprint, to access a system or resource.
  6. Network segmentation: Network segmentation is the process of dividing a network into smaller, isolated segments in order to reduce the risk of data breaches and other security incidents.
  7. Virtual private networks (VPNs): VPNs are secure networks that are created over public networks, such as the internet, in order to provide secure remote access to resources.
  8. Secure sockets layer (SSL) certificates: SSL certificates are digital certificates that are used to establish a secure connection between a web server and a client.
  9. Security information and event management (SIEM) systems: SIEM systems are used to collect, analyze, and manage security-related data from a variety of sources in order to identify potential threats and vulnerabilities.
  10. Intrusion prevention systems (IPS): IPS are security systems that are designed to detect and prevent unauthorized access to computer systems and networks.

What is Air Gap?

What is Air Gap? Jonathan Poland

An air gap is a computer network that is physically isolated from other networks, including the internet. This isolation is designed to protect the network and the sensitive data it contains from external threats and vulnerabilities. Air gaps are commonly used in a variety of contexts to enhance information security, including in military systems, medical devices, secure facilities such as prisons, and critical infrastructure such as nuclear power plants.

For example, a military system might use an air gap to protect sensitive information from being accessed by external parties, such as foreign governments or cybercriminals. Similarly, a hospital might use an air gap to protect patient data and ensure the integrity and reliability of its medical devices. In these cases, the air gap helps to reduce the risk of information security breaches and ensure that the systems and data are protected from external threats.

Basically, air gaps are a basic and effective approach to protecting systems and information from external threats. By physically isolating networks from other networks, organizations can reduce the risk of information security breaches and protect sensitive data from external vulnerabilities. The following are illustrative examples of an air gap.

Standalone System

An air gap can be implemented as a standalone system with no networking capabilities whatsoever. For example, a medical device that contains a microcontroller but has no interface to connect to the outside world.

Offline Storage

Data storage devices that are only connected to computers that are offline. For example, a professional musician with a vault of unreleased material that is stored on encrypted data storage devices in a recording studio. Such devices are only connected to musical instruments and computing devices that have no connection to the internet or outside networks.

Stand-alone Network

A network that connects local devices without any physical way to connect to the internet or unsecured networks. For example, the human resources team of a small regional bank want to restrict confidential employee data to three machines that have no outside network connection. The three machines are networked together and attached to various data storage devices. The machines in the network and attached devices have no wireless networking capabilities and are not connected by wire to the internet or the office’s local area network.

Large Networks

An air gap network isn’t necessarily contained to one site and can be geographically distributed at a global, regional, city or campus scale. For example, a globally distributed control system for a pipeline that is completely isolated from unsecured networks. Large air gap networks are often challenging to physically secure. For example, wireless or wired communications running great distances may be intercepted or manipulated.

Physical Security

A hydroelectric dam maintains control systems that aren’t networked to the outside world. These systems are physically secured in a management office that can only be accessed by authorized individuals with a variety of security measures in place such as an access control system and security system.

Segregation Of Duties

A hedge fund is developing financial trading algorithms in a small room with no network connections out and advanced physical security measures such as a mantrap. They often use segregation of duties to ensure that no one person can remove or bring in data to the room. For example, any updates to the system involve multiple trusted people that have different roles such that no single person could install a malicious file.

Signal Blocking

A nuclear power station is completely unconnected to any networks. Efforts are made to block wireless networking signals in sensitive areas of the facility.

Hardware Validation

Modern hardware may contain networking capabilities that is not well documented. This may be done to implement functionality such as remote support or software updates. Alternatively, networking capabilities may be built into things for malicious purposes. As such, implementing a secure air gap network requires carefully reviewing any hardware that comes into contact with the system including external storage devices and peripherals such as a keyboard.

Updates

Air gap networks can only be updated by connecting outside data storage devices. This is a high risk operation and is a common way for air gapped systems to be compromised. Updates to air gap networks require a secure end-to-end procedure that includes a chain of trust for the files you are using and segregation of duties that ensure no single person can add malicious files. The security of updates are also completely reliant on the integrity of the data storage hardware used.

Backups

Air gap networks are commonly backed up locally on the air gap network itself. Implementation of secure offsite backups requires a process with all the same elements as an update process including hardware validation, chain of trust and segregation of duties. Physical security for data leaving a facility is also an important consideration.

Data Breach

Data Breach Jonathan Poland

A data breach is a security incident in which sensitive, protected, or confidential data is accessed, disclosed, or stolen. Data breaches can occur in a variety of ways, including through cyber attacks, physical theft, insider threats, or accidental disclosures. The consequences of a data breach can be significant, as it can lead to financial losses, reputational damage, and legal liabilities for the affected organization. It can also have serious consequences for the individuals whose data has been compromised, as it can lead to identity theft, financial fraud, and other forms of harm.

There are several steps that organizations can take to prevent and respond to data breaches. These include implementing strong security measures, such as firewalls, encryption, and authentication protocols, as well as establishing policies and procedures for handling sensitive data. It is also important for organizations to have a plan in place for responding to a data breach, including protocols for notification, investigation, and recovery. Overall, data breaches can have serious consequences for organizations and individuals. By implementing strong security measures and having a plan in place for responding to a data breach, organizations can minimize the risk of a data breach occurring and take steps to protect themselves and their customers.

The following are illustrative examples of a data breach.

  • Trade Secrets – An employee emails trade secrets to her friend who isn’t authorized to access it.
  • Contacts – A salesperson loses an folder filled with business cards of customers.
  • Financial Credentials – A retailer loses the financial credentials of millions of customers to an advanced persistent threat.
  • Authentication Credentials – An email service is hacked resulting in the loss of authentication credentials such as passwords.
  • Communications – A video chat app losses videos of millions of personal conversations due to a security flaw in its public API.
  • Medical Data – A doctor sends a patient someone else’s medical data.
  • Personal Data – A virus allows an individual to look at the files on a person’s mobile device including photos, contacts, communications and receipts of financial transactions.
  • Photos – A cloud storage app is hacked resulting in the publication of private photos to the public.
  • Employee Records – A technician loses a backup containing thousands of employee records including highly confidential details such as salary and medical insurance claims.
  • Designs – A computer is stolen from an office that contains the confidential product design of an upcoming mobile device.
  • Financial Data – An employee in accounting mistypes an email address and accidentally sends confidential information regarding a company’s quarterly financial results to an outside domain.
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