top data security threats in ai technology

Today, technology plays an important role in the global transition to a more energy-efficient system. Today we look into top data security threats in AI technology and why it matters. One of the most in-demand technologies these days is artificial intelligence (AI).

what is artificial intelligence AI?

Artificial intelligence is the simulation of human cognitive processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and computer vision.It is a field of computers linked to many disciplines, promising to increase efficiency and a high level of autonomy and automation.

In short, it is a double-edged sword that acts as a catalyst for industrial revolution. It works with Machine Learning (ML) which analyzes a lot of data at the same time, with the aim of finding patterns through algorithms.

As a result, organizations and governments can play a significant role in this combination, increasing innovation and business performance.

AI and its ability to collect and analyze data

It’s no secret that the digital world brings data, making it difficult to manage. It will take a lot of time, effort and resources to do that with meaningful results.

As a result, the world is turning to AI for help, because it can collect and analyze large amounts of data in record time. For many businesses, AI has become essential. Businesses use it to identify their customers on a deeper level.

Using the collected data, AI can predict what customers want to do next, helping businesses deliver a better customer experience. It has greatly improved the online shopping scene as users are now greeted with personalized product recommendations on their pages.

With this personalized experience, customers feel more connected and connected to the brand. But while AI has various benefits, it still poses a plethora of digital, physical and political risks.

top data security threats in ai technology

It is true that AI technology seems to have unlimited potential, but it is also true that it carries cyber risks that should not be ignored. Here are some of the data security risks in the AI ​​industry.

1. Data collection

As an internet user, you may have found yourself searching the internet and seeing advertisements for certain products on social media platforms such as Facebook and Instagram.

Some may find it unique, but sometimes it can be scary because it seems like AI follows you around different platforms. Sometimes, a salesperson with AI technology knows what you want better than you do.

2. Data Privacy Loophole

There are different AI algorithms, each collecting different types of data. Most start with personal information such as your name, age, gender, and location. However, it’s also important to note that sophisticated algorithms can track your buying and spending patterns and the products you’re most interested in.

Of course, there are some countries where carriers can buy your data without your consent.

Social media systems are among the top companies in this area. But unfortunately, they often take advantage of their easy access to update their systems and settings, eventually finding holes in their privacy settings. As a result, many consumers have no idea that their data is exposed to third party carriers.

3. Abuse of power

In the industry, not only marketers can use different data to their advantage. In many cases, AI can also help government agencies locate, profile and locate people with or without their consent. With that in mind, it’s important to understand that giving the government unfettered access to citizens’ privacy won’t stop at the big level.

Because of the abuse of power, some argue that users should have full control over who collects, analyzes, stores and changes their data. At the very least, everyone should know who is using their data and why.

4. Access through AI technology

Smart technology is smart, smart – and it keeps getting smarter. Over time, machine learning is increasing rapidly. Meanwhile, everyone can see that intelligence is slowly changing from artificial to beautiful. And as it gets better, cybercriminals’ ability to use more sophisticated tools and methods increases.

Now, AI-powered malware can easily infect your device through untraceable apps, which are almost impossible to remove. Therefore, the damage is often irreversible.

How to mitigate AI data security vulnerabilities

Government agencies are now looking for data security measures to protect people from unwanted AI cybercrimes. For example, the European Union has mandated the General Data Protection Regulation (GDPR). This gives Internet users the ability to control their personal data, making it impossible for organizations to engage in malicious data collection practices.

In addition, a comprehensive data protection system costs the company up to $20 million. GDPR and best practices cover data that will continue to protect that use

Use good, clean data.

Only the type of data necessary to develop AI should be collected, and data should be kept only as long as necessary to fulfill that purpose.

Use real datasets

Developers should build AI using accurate, precise and representative datasets. Whenever possible, developers should create AI algorithms that will evaluate and guarantee the quality of other algorithms.

To management staff

Users need to know when their data is being used, if AI is making decisions about them, and if their data is being used to create AI. They should also have the option to accept the use of such data.

Reduce algorithmic bias/preferences.

Make sure the dataset is broad and inclusive when teaching AI. Algorithmic conflicts often present challenges for women, minorities, and groups (such as people with voice disabilities, the elderly) who make up only a small part of the technology workforce.

benefits of AI technology

1. Eliminate fatigue from the process

Cybercriminals can be relentless, making little changes in their tactics and spending a lot of time. As mentioned before, taking the defender of this defense can become negative, leading to fatigue and increased human error. AI takes compensation out of the equation by removing systems that don’t understand the concept of fatigue. It’s also scalable, managing repetitive tasks while learning from each piece of data that enters the system.

2. Process big data easily

As more and more companies move their infrastructure to the cloud, there has been a need to protect the unimaginable amount of data exchanged and backed up. We have reached a tipping point in how we can handle everything safely, and the best solution is AI. Automation allows companies to move large amounts of data without worrying about a malicious actor lurking in the thick of it.

3. Automatic notification for new attacks.

Another aspect of AI that improves cybersecurity systems is the real-time nature of detecting and predicting threats. Simply put, AI technology doesn’t need to go through the advanced level of human judgment to decide whether a piece of data is trustworthy or not. Automatic beauty is in its aim, and the AI ​​model triggers the real time when it detects suspicious behavior. In addition, AI predictive models detect risks before they happen.

types of AI

1: The machine works.

These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that defeated Garry Kasparov in 1990. Deep Blue can detect pieces in the board and make predictions, but since it has no memory, it cannot use past experiences. will tell the future.

2: Limited storage

These AI systems have memory, so they can use past experiences to make future decisions. Some of the decision-making tasks in self-driving cars are done this way.

3: Theory of mind.

Theory of mind is a psychological term. Along with AI, this means that the system will have social intelligence to understand emotions. This type of AI will be able to create human intentions and predict behavior, skills necessary for AI systems to become full members of the human community.

4: Self-awareness.

In this model, the AI ​​system has its own sensitivity, which gives them insight. Self-aware machines understand their own current state. This type of AI does not yet exist.