In this how AI is changing cybersecurity article, we are going to talk about the following – What is Artificial Intelligence, how Hackers Are Leveraging Artificial Intelligence (AI) for Cyber Attacks? , how is Armed Artificial Intelligence (AI) being leveraged to enhance cyber-attacks? and how to protect your Armed Artificial Intelligence network?
What is Artificial Intelligence (AI)?
Artificial Intelligence is a technological advance that allows systems to simulate a human-like intelligence going beyond programming specific orders to make autonomous decisions based on standards from massive databases.
The purpose of Armed AI in cyber-attacks is to infiltrate networks and systems faster than most organizations, using the unique capabilities of Artificial Intelligence technologies. Features like information retention learn the intelligence and enhanced speed of automation.
Types of Artificial Intelligence (AI)
Reactive machines follow the most basic AI principles and, as the name suggests, can only use their intelligence to sense and react to the world in front of them. Reactive machines can’t store memory, so they can’t make real-time decisions based on experience.
Awareness of the world directly means that reactive machines are designed to perform only a limited number of specialized tasks. However, deliberately narrowing the worldview of reactive machines is not a cost-cutting measure but instead means that this type of AI will become more reliable and trustworthy. That is, they respond similarly to the same stimulus each time.
A critical example of a reactive machine is Deep Blue, designed by IBM as a chess-playing supercomputer in the 1990s, which beat international grandmaster Gary Kasparov at the game. Deep Blue can only identify pieces on the chessboard, know how each piece moves based on the chess rules, recognize each piece’s current position, and use the most logical Movement to determine something. The computer wasn’t tracking the opponent’s future moves or trying to place his works in better positions. Instead, it was considered a reality of its own.
Another example of a reactive game engine is Google’s AlphaGo. AlphaGo also has the edge over Deep Blue in more complex games, as it cannot assess future moves and relies on its neural networks to determine current game developments. AlphaGo also defeated Go champion Lee Sedol in 2016, beating out world-class opponents in the game.
Although limited in scope and not easily adjustable, reactive machine AI can reach a certain level of complexity and provide reliability when built to perform repeatable tasks.
With limited memory, AI retains historical data and predictions to gather information and consider potential decisions. It looks to the past for clues as to what might happen next. AI with limited memory is more complex and offers greater possibilities for over-reactive machines.
Memory-limited AI occurs when teams continuously train models to analyze and use new data or build AI environments so that models can be automatically trained and updated.
Three major ML models utilize limited memory AI:
- Reinforcement learning: Through trial and error, you can make better predictions.
- Long short-term memory (LSTM): Predict the next item in a sequence using previous data. LTSM considers recent information most important when making forecasts and subtracting historical data but still uses it to conclude.
- Evolutionary generative adversarial networks (E-GAN): It evolves, exploring slightly modified paths based on previous experience with each new decision. Always looking for better ways, this model uses simulations, statistics, or chance to predict outcomes across evolutionary mutation cycles.
Theory of Mind
Theory of mind is only that – theoretical. We have not yet gained the technical and scientific skills necessary to reach this next level of AI.
This concept is focused on the psychological premise of understanding that other living beings have thoughts and feelings that influence their behavior. When it comes to AI machines, this means that AI understands how humans, animals, and other devices are feeling, makes decisions through introspection and determination, and uses that information to make its own decisions. In essence, machines must be able to capture and process many concepts of the “mind,” the emotional fluctuations that accompany decision-making, and other psychological ideas in real-time.
Once the theory of mind is established, the final step for AI at some point in the future is for it to become self-aware. This type of AI has human-level consciousness and understands not only its presence in the world but also the presence and emotional states of others. So, for example, you can understand what other people need based on what they say and how they say it.
Self-awareness in AI relies on human researchers understanding the assumptions of consciousness and learning how to replicate consciousness and incorporate it into machines.
Why Is Artificial Intelligence (AI) vital?
AI has many uses — from increasing vaccine development to automating the detection of potential fraud.
According to CB Insights, Artificial Intelligence (AI) Private markets activity experienced a record year in 2021, with global funding up 108% compared to 2020. AI is rapidly being adopted and has spread to various industries.
Business Insider Intelligence’s 2022 AI in Banking report found that more than half of financial services firms are already using AI solutions to manage risk and generate revenue. Applying AI to banking could save more than $400 billion.
As for healthcare, the integration of AI into healthcare faces challenges, according to a 2021 World Health Organization report, but the technology could lead to more informed healthcare policy and improved patient diagnostic accuracy. In addition, it indicates “great potential” because it can lead to benefits such as improvement.
AI is also building a name for itself in entertainment. According to Grand View Research, the global market for AI in media and entertainment is estimated to reach US$99.48 billion by 2030, from US$10.87 billion in 2021.
How Hackers Are Leveraging Artificial Intelligence (AI) for Cyber Attacks?
In the world of cyber security today, artificial intelligence (AI) has transformed how we come across, respond to and recover from cyber-attacks. But despite the many advances of Artificial Intelligence in cybersecurity, cyber-attacks are becoming more and more dangerous precisely because of Artificial Intelligence.
Cybercriminals are leveraging existing tools of artificial intelligence and AI-based technologies to carry out attacks. As a result, they cause threats, and cyber-attacks are becoming increasingly difficult to prevent.
How is Armed Artificial Intelligence (AI) being leveraged to enhance cyber-attacks?
Traditionally, Artificial Intelligence (AI) technologies have enhanced cybersecurity solutions to improve the overall security posture of business networks. However, as cybercriminals continue to leverage and weaponries AI, they have developed intelligent malicious software programs that execute attacks rapidly.
- Changing Artificial Intelligence Algorithms: Artificial Intelligence allows users to improve speed, efficiency, and accuracy. However, when altered with malicious intent, cybercriminals can create precise and undetectable attacks like phishing attacks at a faster rate.
- Data Poisoning: Cybercriminals can “poison” these datasets by using AI training datasets. In performing so, data is easily manipulated and altered to reflect incorrect information and thus affect the accuracy of an organization’s system.
- Entry Attacks: Cyber criminals can alter entries into Artificial Intelligence networks that trigger unexpected results. For example, an attacker could use hidden codes for benign applications, which are programming combinations to “fire” at a specific time, even months after this information was changed. All this is to maximize the impacts of cyber-attacks, infiltrating an application when it is most vulnerable.
- Mimicking Trusted Systems: Cyber criminals also leverage Artificial Intelligence technologies to develop malicious software capable of emulating a controlled and secure system. As a result, cybercriminals can execute undetectable ransom ware attacks as they integrate into a company’s security network.
Armed Artificial Intelligence has created a space for cybercriminals to accelerate, scale, and increase the impact of hacker attacks to the point where many networks do not have time to adequately protect themselves.
The cybersecurity industry must gain a vast understanding of the techniques used by cybercriminals to generate robust and effective security frameworks and controls to prevent artificial intelligence attacks in the future.
How to protect your Armed Artificial Intelligence network?
Until proper controls are in place to prevent Armed Artificial Intelligence attacks, here are some best practices to ensure your network is always secured.
Keep Cyber Security Hygiene
Maintaining good cyber security hygiene means updating software, managing passwords, limiting administrative access to the network, and more. While there are other best practices, staying up to date with the health of your network can improve incident response time and help prevent future cyber-attacks.
Continuous cybersecurity monitoring of your firm’s network helps uncover weak or vulnerable systems that could fall victim to an Armed Artificial Intelligence attack. Therefore, your company should establish an ongoing monitoring plan to ensure your network can identify and respond to re-attempted threats, apply new controls to mitigate risk and monitor the ecosystem to ensure adequate management.
Security Scorecard Security Ratings
Security Scorecard’s security ratings ensure consistent, data-driven rankings that your organization can use to influence cybersecurity measures and prevent Armed Artificial Intelligence attacks. In addition, security ratings provide an inside view of your security posture, allowing you to take preventative measures even before a cyber-attack.
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