Protecting IT infrastructure from criminals and hackers has always been a daunting task, with new cyber threats emerging almost weekly. However, the rapid development of artificial intelligence (AI) technologies elevates the problem to an entirely new level: in the wrong hands, AI can inflict critical damage on any organization.
In this article, we explore how criminals are leveraging AI to conduct more cunning, systematic, and effective cyber attacks. You'll learn about new methods in hackers' arsenals and ways to protect against them. Sometimes, such knowledge can literally save a business from catastrophe.
The Growing Role of AI in Cyber Attacks: How Dangerous Is It?
The rise of AI technologies has brought not only new opportunities and high hopes but also significant risks. Experts warn about the potential problems associated with the proliferation of artificial intelligence. A recent report by the UK's National Cyber Security Centre (NCSC) notes that within the next two years, AI technologies will almost certainly increase the pace of cyber attacks and amplify their impact. According to the Centre, AI opens up vast opportunities even for criminals lacking technical skills. In other words, hacking has never been more accessible.
This concern is shared in the business community. A 2023 SecurityMagazine survey revealed that 75% of security professionals noted an increase in cyber attacks over the past 12 months. 85% of respondents attribute this growth to attackers using generative AI.
A recent Gigamon survey showed that 82% of security and IT leaders believe the global threat of ransomware attacks will grow as AI is increasingly used in cyber attacks.
However, artificial intelligence can be employed in a variety of crimes—from automated DDoS attacks to sophisticated phishing and social engineering. For example:
- In 2021, McAfee researchers uncovered a cyber espionage campaign called Operation Diànxùn. Criminals used AI to create phishing emails targeting telecommunications companies worldwide. Hackers utilized natural language generation to craft convincing messages resembling typical emails from recruiters or industry experts. However, these emails contained attachments and links with malware.
- In 2023, hackers used AI to bypass the biometric authentication system of the cryptocurrency exchange Bitfinex, which required users to verify their identity through facial and voice recognition. Criminals employed deepfake technology to generate realistic facial images, as well as the victims' voices and behaviors. As a result, the attackers stole digital assets worth $150 million.
- In February 2024, a financial specialist at the international engineering company Arup was deceived by criminals and transferred over $25 million to them. To overcome the manager's doubts about the suspicious transaction, the fraudsters used deepfake technology to generate a real-time video call with the CFO and some other employees.
Consequently, even experienced employees and advanced authentication systems struggle to counter new threats. Moreover, modern AI-powered cyber attacks pose a massive challenge for professionals, as they can change their parameters in real-time, adapting to any countermeasures.
Key Strategies of AI Use in Modern Cyber Attacks
We have already described some real AI-based cyber attacks above. However, these are far from all the possible threats. Strategies for using artificial intelligence with criminal intent can be highly diverse. Let's identify the main vectors of such threats.
Adaptive Phishing Attacks
Criminals utilize AI tools to prepare highly personalized and convincing phishing messages. Such attacks rely on AI algorithms to analyze vast amounts of data from social networks, corporate websites, and other public sources to mimic legitimate communication styles and content, or even replicate the correspondence manner of real company employees.
Moreover, adaptive phishing employs social engineering elements to obtain valuable information about the victim. Unlike traditional phishing attacks that often rely on generic templates, attacks using generative AI can produce context-dependent messages targeting specific individuals. This level of personalization makes it difficult for recipients to distinguish genuine messages from fraudulent ones, giving criminals a much higher chance of success.
Generative AI also allows cybercriminals to bypass traditional security measures aimed at detecting signs of conventional phishing. As a result, security teams face additional challenges in identifying and preventing such attacks using standard tools.
Deepfakes
Not long ago, biometrics and video call access authorization seemed like the most reliable cybersecurity tools. However, everything changed with the development of machine learning and deep learning. AI models can generate virtually any media content: images, videos, audio, etc. A deepfake of a real person can be 100% convincing, mimicking not only the appearance but also the mimicry, voice, and mannerisms of the victim.
Today, deepfake incidents are perhaps the most high-profile cybercrimes involving AI. Fraudsters widely use the technology for financial scams, disinformation, and manipulations that can cause enormous damage to any organization. According to experts, producing a single deepfake costs just one and a half dollars, but by 2024, the global damage from such practices could exceed one trillion dollars.
As practice shows, fraudsters prepare such attacks for months, resorting to social engineering methods. Victims of fake video calls can include not only ordinary employees but also top managers with access to sensitive information and multimillion-dollar assets.
Malware Automation
It's no secret that cybercriminals have learned to use generative AI to create malicious code automatically—quickly and cheaply. Despite tech giants' efforts to prevent the use of their language models for writing malware, hackers constantly find new ways to circumvent restrictions. Entire platforms (like HackedGPT and WormGPT) help criminals create malware, making the dynamics of cyber attacks very complex, dense, and unpredictable.
In the future, AI-based automated cyber threats could be even more serious. For example, cybersecurity experts at Hyas created a "laboratory" virus called BlackMamba, which can dynamically change its code using ChatGPT. In the specialists' experiment, this keylogger worm successfully adapted to any scenario and evaded detection by popular antivirus software. It's hard to imagine how such viruses could complicate the work of cybersecurity professionals and the losses their spread could cause in private and government organizations.
Bypassing Security Systems
AI tools open up vast opportunities for hackers to bypass traditional cybersecurity systems and conduct truly complex attacks.
Malefactors can use machine learning algorithms to automatically scan large amounts of code and systems for vulnerabilities. They gain the ability to generate new exploits on the fly and break into systems even if the discovered vulnerability is temporary and seems insignificant. They also have extensive capabilities for password cracking, whether through brute force or more sophisticated approaches based on statistics and users' personal data.
Ultimately, they can combine technological methods with phishing and social engineering, spreading malicious software using natural language generation and deepfakes.
Organizations' vulnerability to such attacks is only growing. Even if a cybersecurity team uses certain AI tools to detect threats, adversarial machine learning methods increasingly allow hackers to interfere with their work and bypass protection.
Protecting IT infrastructure from criminals and hackers has always been a daunting task, with new cyber threats emerging almost weekly. However, the rapid development of artificial intelligence (AI) technologies elevates the problem to an entirely new level: in the wrong hands, AI can inflict critical damage on any organization.
In this article, we explore how criminals are leveraging AI to conduct more cunning, systematic, and effective cyber attacks. You'll learn about new methods in hackers' arsenals and ways to protect against them. Sometimes, such knowledge can literally save a business from catastrophe.
The Growing Role of AI in Cyber Attacks: How Dangerous Is It?
The rise of AI technologies has brought not only new opportunities and high hopes but also significant risks. Experts warn about the potential problems associated with the proliferation of artificial intelligence. A recent report by the UK's National Cyber Security Centre (NCSC) notes that within the next two years, AI technologies will almost certainly increase the pace of cyber attacks and amplify their impact. According to the Centre, AI opens up vast opportunities even for criminals lacking technical skills. In other words, hacking has never been more accessible.
This concern is shared in the business community. A 2023 SecurityMagazine survey revealed that 75% of security professionals noted an increase in cyber attacks over the past 12 months. 85% of respondents attribute this growth to attackers using generative AI.
A recent Gigamon survey showed that 82% of security and IT leaders believe the global threat of ransomware attacks will grow as AI is increasingly used in cyber attacks.
However, artificial intelligence can be employed in a variety of crimes—from automated DDoS attacks to sophisticated phishing and social engineering. For example:
- In 2021, McAfee researchers uncovered a cyber espionage campaign called Operation Diànxùn. Criminals used AI to create phishing emails targeting telecommunications companies worldwide. Hackers utilized natural language generation to craft convincing messages resembling typical emails from recruiters or industry experts. However, these emails contained attachments and links with malware.
- In 2023, hackers used AI to bypass the biometric authentication system of the cryptocurrency exchange Bitfinex, which required users to verify their identity through facial and voice recognition. Criminals employed deepfake technology to generate realistic facial images, as well as the victims' voices and behaviors. As a result, the attackers stole digital assets worth $150 million.
- In February 2024, a financial specialist at the international engineering company Arup was deceived by criminals and transferred over $25 million to them. To overcome the manager's doubts about the suspicious transaction, the fraudsters used deepfake technology to generate a real-time video call with the CFO and some other employees.
Consequently, even experienced employees and advanced authentication systems struggle to counter new threats. Moreover, modern AI-powered cyber attacks pose a massive challenge for professionals, as they can change their parameters in real-time, adapting to any countermeasures.
Key Strategies of AI Use in Modern Cyber Attacks
We have already described some real AI-based cyber attacks above. However, these are far from all the possible threats. Strategies for using artificial intelligence with criminal intent can be highly diverse. Let's identify the main vectors of such threats.
Attacks on Machine Learning Algorithms
Today, businesses and organizations are massively implementing various AI tools in their daily operations to be more efficient. Malefactors have gained a new field of activity: they increasingly interfere with algorithms to disrupt operations or gain unauthorized access to system resources. This practice is called adversarial machine learning. Its essence lies in misleading AI models by using special datasets or other tools.
Methods of adversarial attacks typically include evasion attacks, poisoning attacks, and model extraction:
- Poisoning Attack: Carried out during the training or retraining phase of an AI model. Introducing "poisoned" data at this phase causes the model to generate false results.
- Evasion Attack: The most common method. A hacker manipulates data at the deployment stage of the model to bring it down. A typical example of evasion attacks is spoofing attacks, which increasingly affect biometric verification systems.
- Model Extraction Attack: Based on the attacker's ability to reconstruct the AI model by extracting the data on which it was trained. Malefactors can not only copy your product but also discover vulnerabilities for more insidious cyber attacks.
Malware to Overcome Protection
With the advent of AI, malware developers have gained the ability to use algorithms to automate and enhance their attacks at all levels—from preliminary reconnaissance to evasion from detection. AI-based cyber attacks have become more cunning and targeted—they can easily bypass traditional security systems.
In the world of cyber threats, familiar viruses and firewalls are rapidly losing effectiveness. Traditional security tools find it difficult to detect AI-based malicious code due to its dynamic and polymorphic nature. For example, an AI-based "worm" can mutate in real-time, hiding its presence in the system for weeks or months. Even a few hours of such software operating in a corporate network can lead to massive data compromise with unpredictable consequences.
Security teams face significant challenges in detecting and isolating these threats, especially in real-time.
Automated DDoS Attacks
In recent years, automation and scripting have made Denial-of-Service (DDoS) attacks quite simple to implement. The entry barrier is lower than ever: even a user without special technical skills can participate in DDoS processes and, under certain circumstances, cause significant harm to the target system.
However, the introduction of AI tools is an entirely different level of automation. Malefactors can now process huge volumes of security telemetry data. This continuous analysis allows optimizing attack techniques in real-time, flexibly distributing resources to achieve a destructive effect. AI technologies enable DDoS attacks to be masked as regular traffic with exceptional efficiency, making counteracting them extremely challenging.
AI-based automated DDoS attacks differ from traditional ones by their ease of scaling, flexibility, and quality of masking. The low entry threshold and high efficiency of AI can lead to a significant increase in the number of DDoS attacks, burdening networks and specialists, even if they are relatively simple.
Persistent High-Complexity Threats
Considering AI cyber threats in a broader context, artificial intelligence creates an entirely new landscape of risks for IT infrastructure. Hackers now rely on collecting and processing huge volumes of data in real-time to find system vulnerabilities and plan attacks with surgical precision. They can generate malicious code almost continuously and create network worms that constantly mutate, avoiding detection for months. This is real cyber weaponry capable of stealing data, disrupting systems, and sowing chaos.
These chronic top-level threats have no simple solution today. Organizations, IT departments, and security teams will have to adapt to these realities to build their defense strategies accordingly. Modern cybersecurity architecture should be based on flexibility, a proactive approach, the implementation of advanced technologies, and cooperation with private IT teams and government institutions.
How to Protect Against Cyber Attacks Using Artificial Intelligence: Key Strategies
New AI-based cyber threats are unlike the challenges security specialists have faced before. Responding to these risks requires new approaches and technologies. Here are the main principles on which a modern security strategy should be built:
Enhanced Staff Training
Employees must be aware of new threats like high-quality personalized phishing emails, AI bots, and deepfakes capable of convincingly imitating human behavior online. Specialists should be trained to recognize such tools and respond appropriately. Fostering a cybersecurity culture within the team is crucial; staff should understand their role in protection and report any system anomalies.
Use of Integrated Security Systems
Modern integrated cybersecurity systems combine a range of security tools and technologies into a unified logic. They protect the company at multiple levels—from external perimeter dangers to internal risks. Integrated systems include AI-based tools that effectively detect intrusions, identify vulnerabilities, manage resource access, and consolidate data from different security points in real-time.
Implementation of Multi-Level Protection Mechanisms
A multidimensional approach to cybersecurity is based on the idea that no single protection mechanism is foolproof. The threat counteraction strategy should be deep and rely on multiple levels of threat control, each complicating the task for malefactors. A multi-level strategy operates on three levels: network security, endpoint security, and account security. Combining these levels allows organizations to create a strong and reliable protection system capable of timely responding to any threats.
Traffic Analysis and Monitoring
Without high-quality real-time traffic monitoring, it's impossible to ensure a secure system perimeter, especially against dynamic and deceptive AI-based threats. Today's log analysis, packet inspection, and other monitoring methods should rely on proprietary machine learning algorithms to detect suspicious activity and intrusion attempts early on. Only AI tools can provide the necessary speed and accuracy in monitoring.
System Updates and Patches
Malefactors have learned to use AI to generate vast amounts of malicious code and find system vulnerabilities through data analysis. Cybersecurity teams must keep pace with these developments. Continuous threat monitoring and penetration testing should form the basis for regular and timely security patches. Even if not all vulnerabilities can be eliminated, complicating the attackers' task and gaining time for response is crucial.
Incident Response
Organizations must have a clear plan for responding to AI-driven cyber attacks. It's important to have effective anomaly detection systems and to counter threats instantly by isolating damaged systems and suspending suspicious processes. Having data backups and backup communication channels is essential for maintaining operations during an attack. After resolving the incident, conducting a thorough analysis and drawing conclusions for the future is vital.
Standards and Recommendations to Minimize AI Cyber Attack Risks
Leading cybersecurity experts and government regulators are well aware of the threats posed by AI-based attacks on software. They have developed industry recommendations and cybersecurity standards. Compliance with these is the best solution for IT infrastructure security and regulatory compliance.
International ISO Standards
The International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) have established standards in the field of information technology:
- ISO/IEC 2382:2015: A technical vocabulary created to simplify international cooperation in information technology. The 2015 version includes key technical definitions on information and cybersecurity and personal data protection. The standard is currently under review for future updates.
- ISO/IEC 27001: The most well-known standard for implementing information security management systems, last updated in 2022. It provides guidance on creating, implementing, maintaining, and improving a cybersecurity system. Compliance ensures the implementation of recognized industry practices and data protection principles.
- ISO/IEC 27005: Offers methodological guidelines for data security and risk management, helping organizations meet ISO/IEC 27001 requirements. It aids in defining context, assessing risks, monitoring, and determining acceptable risk levels in data handling.
- ISO/IEC 26514: Defines principles for designing and developing user information in systems and software engineering products. It guides interface designers, usability specialists, and content developers to present data accessibly and securely throughout the product life cycle.
Protecting IT infrastructure from criminals and hackers has always been a daunting task, with new cyber threats emerging almost weekly. However, the rapid development of artificial intelligence (AI) technologies elevates the problem to an entirely new level: in the wrong hands, AI can inflict critical damage on any organization.
In this article, we explore how criminals are leveraging AI to conduct more cunning, systematic, and effective cyber attacks. You'll learn about new methods in hackers' arsenals and ways to protect against them. Sometimes, such knowledge can literally save a business from catastrophe.
The Growing Role of AI in Cyber Attacks: How Dangerous Is It?
The rise of AI technologies has brought not only new opportunities and high hopes but also significant risks. Experts warn about the potential problems associated with the proliferation of artificial intelligence. A recent report by the UK's National Cyber Security Centre (NCSC) notes that within the next two years, AI technologies will almost certainly increase the pace of cyber attacks and amplify their impact. According to the Centre, AI opens up vast opportunities even for criminals lacking technical skills. In other words, hacking has never been more accessible.
This concern is shared in the business community. A 2023 SecurityMagazine survey revealed that 75% of security professionals noted an increase in cyber attacks over the past 12 months. 85% of respondents attribute this growth to attackers using generative AI.
A recent Gigamon survey showed that 82% of security and IT leaders believe the global threat of ransomware attacks will grow as AI is increasingly used in cyber attacks.
However, artificial intelligence can be employed in a variety of crimes—from automated DDoS attacks to sophisticated phishing and social engineering. For example:
- In 2021, McAfee researchers uncovered a cyber espionage campaign called Operation Diànxùn. Criminals used AI to create phishing emails targeting telecommunications companies worldwide. Hackers utilized natural language generation to craft convincing messages resembling typical emails from recruiters or industry experts. However, these emails contained attachments and links with malware.
- In 2023, hackers used AI to bypass the biometric authentication system of the cryptocurrency exchange Bitfinex, which required users to verify their identity through facial and voice recognition. Criminals employed deepfake technology to generate realistic facial images, as well as the victims' voices and behaviors. As a result, the attackers stole digital assets worth $150 million.
- In February 2024, a financial specialist at the international engineering company Arup was deceived by criminals and transferred over $25 million to them. To overcome the manager's doubts about the suspicious transaction, the fraudsters used deepfake technology to generate a real-time video call with the CFO and some other employees.
Consequently, even experienced employees and advanced authentication systems struggle to counter new threats. Moreover, modern AI-powered cyber attacks pose a massive challenge for professionals, as they can change their parameters in real-time, adapting to any countermeasures.
Key Strategies of AI Use in Modern Cyber Attacks
We have already described some real AI-based cyber attacks above. However, these are far from all the possible threats. Strategies for using artificial intelligence with criminal intent can be highly diverse. Let's identify the main vectors of such threats.
Attacks on Machine Learning Algorithms
Today, businesses and organizations are massively implementing various AI tools in their daily operations to be more efficient. Malefactors have gained a new field of activity: they increasingly interfere with algorithms to disrupt operations or gain unauthorized access to system resources. This practice is called adversarial machine learning. Its essence lies in misleading AI models by using special datasets or other tools.
Methods of adversarial attacks typically include evasion attacks, poisoning attacks, and model extraction:
- Poisoning Attack: Carried out during the training or retraining phase of an AI model. Introducing "poisoned" data at this phase causes the model to generate false results.
- Evasion Attack: The most common method. A hacker manipulates data at the deployment stage of the model to bring it down. A typical example of evasion attacks is spoofing attacks, which increasingly affect biometric verification systems.
- Model Extraction Attack: Based on the attacker's ability to reconstruct the AI model by extracting the data on which it was trained. Malefactors can not only copy your product but also discover vulnerabilities for more insidious cyber attacks.
Malware to Overcome Protection
With the advent of AI, malware developers have gained the ability to use algorithms to automate and enhance their attacks at all levels—from preliminary reconnaissance to evasion from detection. AI-based cyber attacks have become more cunning and targeted—they can easily bypass traditional security systems.
In the world of cyber threats, familiar viruses and firewalls are rapidly losing effectiveness. Traditional security tools find it difficult to detect AI-based malicious code due to its dynamic and polymorphic nature. For example, an AI-based "worm" can mutate in real-time, hiding its presence in the system for weeks or months. Even a few hours of such software operating in a corporate network can lead to massive data compromise with unpredictable consequences.
Security teams face significant challenges in detecting and isolating these threats, especially in real-time.
Automated DDoS Attacks
In recent years, automation and scripting have made Denial-of-Service (DDoS) attacks quite simple to implement. The entry barrier is lower than ever: even a user without special technical skills can participate in DDoS processes and, under certain circumstances, cause significant harm to the target system.
However, the introduction of AI tools is an entirely different level of automation. Malefactors can now process huge volumes of security telemetry data. This continuous analysis allows optimizing attack techniques in real-time, flexibly distributing resources to achieve a destructive effect. AI technologies enable DDoS attacks to be masked as regular traffic with exceptional efficiency, making counteracting them extremely challenging.
AI-based automated DDoS attacks differ from traditional ones by their ease of scaling, flexibility, and quality of masking. The low entry threshold and high efficiency of AI can lead to a significant increase in the number of DDoS attacks, burdening networks and specialists, even if they are relatively simple.
Persistent High-Complexity Threats
Considering AI cyber threats in a broader context, artificial intelligence creates an entirely new landscape of risks for IT infrastructure. Hackers now rely on collecting and processing huge volumes of data in real-time to find system vulnerabilities and plan attacks with surgical precision. They can generate malicious code almost continuously and create network worms that constantly mutate, avoiding detection for months. This is real cyber weaponry capable of stealing data, disrupting systems, and sowing chaos.
These chronic top-level threats have no simple solution today. Organizations, IT departments, and security teams will have to adapt to these realities to build their defense strategies accordingly. Modern cybersecurity architecture should be based on flexibility, a proactive approach, the implementation of advanced technologies, and cooperation with private IT teams and government institutions.
How to Protect Against Cyber Attacks Using Artificial Intelligence: Key Strategies
New AI-based cyber threats are unlike the challenges security specialists have faced before. Responding to these risks requires new approaches and technologies. Here are the main principles on which a modern security strategy should be built:
Enhanced Staff Training
Employees must be aware of new threats like high-quality personalized phishing emails, AI bots, and deepfakes capable of convincingly imitating human behavior online. Specialists should be trained to recognize such tools and respond appropriately. Fostering a cybersecurity culture within the team is crucial; staff should understand their role in protection and report any system anomalies.
Use of Integrated Security Systems
Modern integrated cybersecurity systems combine a range of security tools and technologies into a unified logic. They protect the company at multiple levels—from external perimeter dangers to internal risks. Integrated systems include AI-based tools that effectively detect intrusions, identify vulnerabilities, manage resource access, and consolidate data from different security points in real-time.
Implementation of Multi-Level Protection Mechanisms
A multidimensional approach to cybersecurity is based on the idea that no single protection mechanism is foolproof. The threat counteraction strategy should be deep and rely on multiple levels of threat control, each complicating the task for malefactors. A multi-level strategy operates on three levels: network security, endpoint security, and account security. Combining these levels allows organizations to create a strong and reliable protection system capable of timely responding to any threats.
Traffic Analysis and Monitoring
Without high-quality real-time traffic monitoring, it's impossible to ensure a secure system perimeter, especially against dynamic and deceptive AI-based threats. Today's log analysis, packet inspection, and other monitoring methods should rely on proprietary machine learning algorithms to detect suspicious activity and intrusion attempts early on. Only AI tools can provide the necessary speed and accuracy in monitoring.
System Updates and Patches
Malefactors have learned to use AI to generate vast amounts of malicious code and find system vulnerabilities through data analysis. Cybersecurity teams must keep pace with these developments. Continuous threat monitoring and penetration testing should form the basis for regular and timely security patches. Even if not all vulnerabilities can be eliminated, complicating the attackers' task and gaining time for response is crucial.
Incident Response
Organizations must have a clear plan for responding to AI-driven cyber attacks. It's important to have effective anomaly detection systems and to counter threats instantly by isolating damaged systems and suspending suspicious processes. Having data backups and backup communication channels is essential for maintaining operations during an attack. After resolving the incident, conducting a thorough analysis and drawing conclusions for the future is vital.
Standards and Recommendations to Minimize AI Cyber Attack Risks
Leading cybersecurity experts and government regulators are well aware of the threats posed by AI-based attacks on software. They have developed industry recommendations and cybersecurity standards. Compliance with these is the best solution for IT infrastructure security and regulatory compliance.
International ISO Standards
The International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) have established standards in the field of information technology:
- ISO/IEC 2382:2015: A technical vocabulary created to simplify international cooperation in information technology. The 2015 version includes key technical definitions on information and cybersecurity and personal data protection. The standard is currently under review for future updates.
- ISO/IEC 27001: The most well-known standard for implementing information security management systems, last updated in 2022. It provides guidance on creating, implementing, maintaining, and improving a cybersecurity system. Compliance ensures the implementation of recognized industry practices and data protection principles.
- ISO/IEC 27005: Offers methodological guidelines for data security and risk management, helping organizations meet ISO/IEC 27001 requirements. It aids in defining context, assessing risks, monitoring, and determining acceptable risk levels in data handling.
- ISO/IEC 26514: Defines principles for designing and developing user information in systems and software engineering products. It guides interface designers, usability specialists, and content developers to present data accessibly and securely throughout the product life cycle.
Ethical and Responsibility Guidelines
The global community is moving toward defining ethical boundaries for AI applications in various tasks. Adhering to recommendations from international organizations and industry associations is crucial for creating a safe cyberspace.
- IEEE Ethically Aligned Design: The Institute of Electrical and Electronics Engineers (IEEE) is one of the world's largest professional technical associations. Its members aim for the humanistic and ethical development of new technologies. The Ethically Aligned Design initiative seeks to make autonomous and intelligent systems safe. It provides scientific analysis, resources, fundamental principles, and practical recommendations for creating and maintaining secure intelligent systems. Moreover, the IEEE guideline offers specific directions on standards, certification, and regulation of AI-based products.
- OECD Principles on Artificial Intelligence: The Organisation for Economic Co-operation and Development (OECD) unites 38 developed countries that recognize the principles of liberalism and market economy. In response to the rapid development of AI technologies, OECD experts formulated basic principles for artificial intelligence application. Specifically, AI-based products should be developed and deployed in compliance with the rule of law and human rights (e.g., personal data protection). Organizations and individuals developing, deploying, or operating AI systems should bear full responsibility for their proper and safe functioning.
- EU Guidelines for Trustworthy AI: The European Commission has also developed its own guide for safe AI implementation. According to EC recommendations, an AI-based product should, throughout its life cycle, meet legality requirements (not contradict existing laws), be ethical (not contradict humanistic values), and be technically and socially reliable so it cannot be used to harm humans. Ideally, these three components should work in symbiosis to ensure safety. Additionally, EU countries have strict personal data protection requirements under the GDPR and national laws.
Undoubtedly, no standards, guidelines, or declarations can ensure cybersecurity on their own. However, they provide organizations and specialists with the right development direction to counter new threats.
If you're looking for a team with such experience to implement a secure IT solution, you've come to the right place. Contact our specialists for a consultation right now. They will be happy to study your problem, share their experience, and suggest optimal cybersecurity solutions.
Conclusion
Artificial intelligence is akin to nuclear energy. This technology has brought incredible opportunities but has also created enormous risks. In the hands of criminals, new technologies turn into weapons: AI-powered security attacks can cause catastrophic damage to any digital infrastructure.
Therefore, organizations and cybersecurity teams should take new risks extremely seriously. Mastering the latest AI-based protection technologies, advanced staff training, restructuring security strategies according to the latest standards and expert recommendations—all these steps should become a priority for most businesses right now. However, not every organization has the necessary resources and experience to protect itself independently. In such cases, the best solution is to turn to an experienced IT team with relevant portfolios.
