As computing technology evolved and the concept of linking multiple computers together into a “network” that could share data came into being, it was clear that a model was needed to define and enable those connections. Enter the OSI model in computer network idea.


This model allows various devices and software to “communicate” with one another by creating a set of universal rules and functions. Let’s dig into what the model entails.


History of the OSI Model


In the late 1970s, the continued development of computerized technology saw many companies start to introduce their own systems. These systems stood alone from others. For example, a computer at Retailer A has no way to communicate with a computer at Retailer B, with neither computer being able to communicate with the various vendors and other organizations within the retail supply chain.


Clearly, some way of connecting these standalone systems was needed, leading to researchers from France, the U.S., and the U.K. splitting into two groups – The International Organization for Standardization and the International Telegraph and Telephone Consultive Committee.


In 1983, these two groups merged their work to create “The Basic Reference Model for Open Systems Interconnection (OSI).” This model established industry standards for communication between networked devices, though the path to OSI’s implementation wasn’t as clear as it could have been. The 1980s and 1990s saw the introduction of another model – The TCP IP model – which competed against the OSI model for supremacy. TCP/IP gained so much traction that it became the cornerstone model for the then-budding internet, leading to the OSI model in computer network applications falling out of favor in many sectors. Despite this, the OSI model is still a valuable reference point for students who want to learn more about networking and still have some practical uses in industry.


The OSI Reference Model


The OSI model works by splitting the concept of computers communicating with one another into seven computer network layers (defined below), each offering standardized rules for its specific function. During the rise of the OSI model, these layers worked in concert, allowing systems to communicate as long as they followed the rules.


Though the OSI model in computer network applications has fallen out of favor on a practical level, it still offers several benefits:


  • The OSI model is perfect for teaching network architecture because it defines how computers communicate.
  • OSI is a layered model, with separation between each layer, so one layer doesn’t affect the operation of any other.
  • The OSI model offers flexibility because of the distinctions it makes between layers, with users being able to replace protocols in any layer without worrying about how they’ll impact the other layers.

The 7 Layers of the OSI Model


The OSI reference model in computer network teaching is a lot like an onion. It has several layers, each standing alone but each needing to be peeled back to get a result. But where peeling back the layers of an onion gets you a tasty ingredient or treat, peeling them back in the OSI model delivers a better understanding of networking and the protocols that lie behind it.


Each of these seven layers serves a different function.


Layer 1: Physical Layer


Sitting at the lowest level of the OSI model, the physical layer is all about the hows and wherefores of transmitting electrical signals from one device to another. Think of it as the protocols needed for the pins, cables, voltages, and every other component of a physical device if said device wants to communicate with another that uses the OSI model.


Layer 2: Data Link Layer


With the physical layer in place, the challenge shifts to transmitting data between devices. The data layer defines how node-to-node transfer occurs, allowing for the packaging of data into “frames” and the correction of errors that may happen in the physical layer.


The data layer has two “sub-layers” of its own:


  • MAC – Media Access Controls that offer multiplexing and flow control to govern a device’s transmissions over an OSI network.
  • LLC – Logical Link Controls that offer error control over the physical media (i.e., the devices) used to transmit data across a connection.

Layer 3: Network Layer


The network layer is like an intermediary between devices, as it accepts “frames” from the data layer and sends them on their way to their intended destination. Think of this layer as the postal service of the OSI model in computer network applications.



Layer 4: Transport Layer


If the network layer is a delivery person, the transport layer is the van that the delivery person uses to carry their parcels (i.e., data packets) between addresses. This layer regulates the sequencing, sizing, and transferring of data between hosts and systems. TCP (Transmission Control Protocol) is a good example of a transport layer in practical applications.


Layer 5: Session Layer


When one device wants to communicate with another, it sets up a “session” in which the communication takes place, similar to how your boss may schedule a meeting with you when they want to talk. The session layer regulates how the connections between machines are set up and managed, in addition to providing authorization controls to ensure no unwanted devices can interrupt or “listen in” on the session.


Layer 6: Presentation Layer


Presentation matters when sending data from one system to another. The presentation layer “pretties up” data by formatting and translating it into a syntax that the recipient’s application accepts. Encryption and decryption is a perfect example, as a data packet can be encrypted to be unreadable to anybody who intercepts it, only to be decrypted via the presentation layer so the intended recipient can see what the data packet contains.


Layer 7: Application Layer


The application layer is a front end through which the end user can interact with everything that’s going on behind the scenes in the network. It’s usually a piece of software that puts a user-friendly face on a network. For instance, the Google Chrome web browser is an application layer for the entire network of connections that make up the internet.


Interactions Between OSI Layers


Though each of the OSI layers in computer networks is independent (lending to the flexibility mentioned earlier), they must also interact with one another to make the network functional.


We see this most obviously in the data encapsulation and de-encapsulation that occurs in the model. Encapsulation is the process of adding information to a data packet as it travels, with de-encapsulation being the method used to remove that data added data so the end user can read what was originally sent. The previously mentioned encryption and decryption of data is a good example.


That process of encapsulation and de-encapsulation defines how the OSI model works. Each layer adds its own little “flavor” to the transmitted data packet, with each subsequent layer either adding something new or de-encapsulating something previously added so it can read the data. Each of these additions and subtractions is governed by the protocols set within each layer. A perfect network can only exist if these protocols properly govern data transmission, allowing for communication between each layer.


Real-World Applications of the OSI Model


There’s a reason why the OSI model in computer network study is often called a “reference” model – though important, it was quickly replaced with other models. As a result, you’ll rarely see the OSI model used as a way to connect devices, with TCP/IP being far more popular. Still, there are several practical applications for the OSI model.


Network Troubleshooting and Diagnostics


Given that some modern computer networks are unfathomably complex, picking out a single error that messes up the whole communication process can feel like navigating a minefield. Every wrong step causes something else to blow up, leading to more problems than you solve. The OSI model’s layered approach offers a way to break down the different aspects of a network to make it easier to identify problems.


Network Design and Implementation


Though the OSI model has few practical purposes, as a theoretical model it’s often seen as the basis for all networking concepts that came after. That makes it an ideal teaching tool for showcasing how networks are designed and implemented. Some even refer to the model when creating networks using other models, with the layered approach helping understand complex networks.


Enhancing Network Security


The concept of encapsulation and de-encapsulation comes to the fore again here (remember – encryption), as this concept shows us that it’s dangerous to allow a data packet to move through a network with no interactions. The OSI model shows how altering that packet as it goes on its journey makes it easier to protect data from unwanted eyes.



Limitations and Criticisms of the OSI Model


Despite its many uses as a teaching tool, the OSI model in computer network has limitations that are the reasons why it sees few practical applications:


  • Complexity – As valuable as the layered approach may be to teaching networks, it’s often too complex to execute in practice.
  • Overlap – The very flexibility that makes OSI great for people who want more control over their networks can come back to bite the model. The failure to implement proper controls and protocols can lead to overlap, as can the layered approach itself. Each of the computer network layers needs the others to work.
  • The Existence of Alternatives – The OSI model walked so other models could run, establishing many fundamental networking concepts that other models executed better in practical terms. Again, the massive network known as the internet is a great example, as it uses the TCP/IP model to reduce complexity and more effectively transmit data.

Use the OSI Reference Model in Computer Network Applications


Though it has little practical application in today’s world, the OSI model in computer network terms is a theoretical model that played a crucial role in establishing many of the “rules” of networking still used today. Its importance is still recognized by the fact that many computing courses use the OSI model to teach the fundamentals of networks.


Think of learning about the OSI model as being similar to laying the foundations for a house. You’ll get to grips with the basic concepts of how networks work, allowing you to build up your knowledge by incorporating both current networking technology and future advancements to become a networking specialist.

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Wired: Think Twice Before Creating That ChatGPT Action Figure
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
May 12, 2025 6 min read

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  • Wired, published on May 01st, 2025

People are using ChatGPT’s new image generator to take part in viral social media trends. But using it also puts your privacy at risk—unless you take a few simple steps to protect yourself.

By Kate O’Flaherty

At the start of April, an influx of action figures started appearing on social media sites including LinkedIn and X. Each figure depicted the person who had created it with uncanny accuracy, complete with personalized accessories such as reusable coffee cups, yoga mats, and headphones.

All this is possible because of OpenAI’s new GPT-4o-powered image generator, which supercharges ChatGPT’s ability to edit pictures, render text, and more. OpenAI’s ChatGPT image generator can also create pictures in the style of Japanese animated film company Studio Ghibli—a trend that quickly went viral, too.

The images are fun and easy to make—all you need is a free ChatGPT account and a photo. Yet to create an action figure or Studio Ghibli-style image, you also need to hand over a lot of data to OpenAI, which could be used to train its models.

Hidden Data

The data you are giving away when you use an AI image editor is often hidden. Every time you upload an image to ChatGPT, you’re potentially handing over “an entire bundle of metadata,” says Tom Vazdar, area chair for cybersecurity at Open Institute of Technology. “That includes the EXIF data attached to the image file, such as the time the photo was taken and the GPS coordinates of where it was shot.”

OpenAI also collects data about the device you’re using to access the platform. That means your device type, operating system, browser version, and unique identifiers, says Vazdar. “And because platforms like ChatGPT operate conversationally, there’s also behavioral data, such as what you typed, what kind of images you asked for, how you interacted with the interface and the frequency of those actions.”

It’s not just your face. If you upload a high-resolution photo, you’re giving OpenAI whatever else is in the image, too—the background, other people, things in your room and anything readable such as documents or badges, says Camden Woollven, group head of AI product marketing at risk management firm GRC International Group.

This type of voluntarily provided, consent-backed data is “a gold mine for training generative models,” especially multimodal ones that rely on visual inputs, says Vazdar.

OpenAI denies it is orchestrating viral photo trends as a ploy to collect user data, yet the firm certainly gains an advantage from it. OpenAI doesn’t need to scrape the web for your face if you’re happily uploading it yourself, Vazdar points out. “This trend, whether by design or a convenient opportunity, is providing the company with massive volumes of fresh, high-quality facial data from diverse age groups, ethnicities, and geographies.”

OpenAI says it does not actively seek out personal information to train models—and it doesn’t use public data on the internet to build profiles about people to advertise to them or sell their data, an OpenAI spokesperson tells WIRED. However, under OpenAI’s current privacy policy, images submitted through ChatGPT can be retained and used to improve its models.

Any data, prompts, or requests you share helps teach the algorithm—and personalized information helps fine tune it further, says Jake Moore, global cybersecurity adviser at security outfit ESET, who created his own action figure to demonstrate the privacy risks of the trend on LinkedIn.

Uncanny Likeness

In some markets, your photos are protected by regulation. In the UK and EU, data-protection regulation including the GDPR offer strong protections, including the right to access or delete your data. At the same time, use of biometric data requires explicit consent.

However, photographs become biometric data only when processed through a specific technical means allowing the unique identification of a specific individual, says Melissa Hall, senior associate at law firm MFMac. Processing an image to create a cartoon version of the subject in the original photograph is “unlikely to meet this definition,” she says.

Meanwhile, in the US, privacy protections vary. “California and Illinois are leading with stronger data protection laws, but there is no standard position across all US states,” says Annalisa Checchi, a partner at IP law firm Ionic Legal. And OpenAI’s privacy policy doesn’t contain an explicit carve-out for likeness or biometric data, which “creates a grey area for stylized facial uploads,” Checchi says.

The risks include your image or likeness being retained, potentially used to train future models, or combined with other data for profiling, says Checchi. “While these platforms often prioritize safety, the long-term use of your likeness is still poorly understood—and hard to retract once uploaded.”

OpenAI says its users’ privacy and security is a top priority. The firm wants its AI models to learn about the world, not private individuals, and it actively minimizes the collection of personal information, an OpenAI spokesperson tells WIRED.

Meanwhile, users have control over how their data is used, with self-service tools to access, export, or delete personal information. You can also opt out of having content used to improve models, according to OpenAI.

ChatGPT Free, Plus, and Pro users can control whether they contribute to future model improvements in their data controls settings. OpenAI does not train on ChatGPT Team, Enterprise, and Edu customer data⁠ by default, according to the company.

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LADBible and Yahoo News: Viral AI trend could present huge privacy concerns, says expert
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
May 12, 2025 4 min read

Source:


You’ve probably seen them all over Instagram

By James Moorhouse

Experts have warned against participating in a viral social media trend which sees people use ChatGPT to create an action figure version of themselves.

If you’ve spent any time whatsoever doomscrolling on Instagram or TikTok or dare I say it, LinkedIn recently, you’ll be all too aware of the viral trend.

Obviously, there’s nothing more entertaining and frivolous than seeing AI generated versions of your co-workers and their cute little laptops and piña coladas, but it turns out that it might not be the best idea to take part.

There may well be some benefits to artificial intelligence but often it can produce some pretty disturbing results. Earlier this year, a lad from Norway sued ChatGPT after it falsely claimed he had been convicted of killing two of his kids.

Unfortunately, if you don’t like AI, then you’re going to have to accept that it’s going to become a regular part of our lives. You only need to look at WhatsApp or Facebook messenger to realise that. But it’s always worth saying please and thank you to ChatGPT just in case society does collapse and the AI robots take over, in the hope that they treat you mercifully. Although it might cost them a little more electricity.

Anyway, in case you’re thinking of getting involved in this latest AI trend and sharing your face and your favourite hobbies with a high tech robot, maybe don’t. You don’t want to end up starring in your own Netflix series, à la Black Mirror.

Tom Vazdar, area chair for cybersecurity at Open Institute of Technology, spoke with Wired about some of the dangers of sharing personal details about yourself with AI.

Every time you upload an image to ChatGPT, you’re potentially handing over ‘an entire bundle of metadata’ he revealed.

Vazdar added: “That includes the EXIF data attached to the image file, such as the time the photo was taken and the GPS coordinates of where it was shot.

“Because platforms like ChatGPT operate conversationally, there’s also behavioural data, such as what you typed, what kind of images you asked for, how you interacted with the interface and the frequency of those actions.”

Essentially, if you upload a photo of your face, you’re not just giving AI access to your face, but also the whatever is in the background, such as the location or other people that might feature.

Vazdar concluded: “This trend, whether by design or a convenient opportunity, is providing the company with massive volumes of fresh, high-quality facial data from diverse age groups, ethnicities, and geographies.”

While we’re at it, maybe stop using ChatGPT for your university essays and general basic questions you can find the answer to on Google as well. The last thing you need is AI knowing you don’t know how to do something basic if it does takeover the world.

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