AI in cybersecurity: understanding the risks

AI, short for Artificial Intelligence, refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. It involves various techniques and algorithms that enable computers to analyze data, make decisions, and perform tasks that typically require human intelligence, leading to advancements in cyber security, while also creating risks.  


AI in Cyber Security: Risks of AI

Artificial intelligence (AI) has been enhancing cyber security tools for years. For example, machine learning tools have made network security, anti-malware, and fraud-detection software more potent by finding anomalies much faster than human beings. However, AI has also posed a risk to cyber security. Brute force, denial of service (DoS), and social engineering attacks are just some examples of threats utilizing AI.

The risks of artificial intelligence to cyber security are expected to increase rapidly with AI tools becoming cheaper and more accessible. For example, you can trick ChatGPT into writing malicious code or a letter from Elon Musk requesting donations,

You can also use a number of deepfake tools to create surprisingly convincing fake audio tracks or video clips with very little training data. There are also growing privacy concerns as more users grow comfortable sharing sensitive information with AI.

Read this in-depth guide for more on:

  1. AI Definition.
  2. Artificial intelligence risks.
  3. AI in cyber security.
  4. AI and privacy risks.

What is AI: Artificial Intelligence

AI, or Artificial Intelligence, refers to the development of computer systems that can perform tasks and make decisions that typically require human intelligence. It involves creating algorithms and models that enable machines to learn from data, recognize patterns, and adapt to new information or situations.

In simple terms, AI is like teaching computers to think and learn like humans. It allows machines to process and analyze large amounts of data, identify patterns or anomalies, and make predictions or decisions based on that information. AI can be used in various applications, such as image and speech recognition, natural language processing, robotics, and cybersecurity, to name a few.

Overall, AI aims to mimic human intelligence to solve complex problems, automate tasks, and enhance efficiency and accuracy in different fields.

Machine learning and deep learning 

Machine learning (ML) is a commonly used subset of AI. ML algorithms and techniques allow systems to learn from data and make decisions without being explicitly programmed.

Deep learning (DL) is a subset of ML that leverages artificial computational models inspired by the human brain called neural networks for more advanced tasks. ChatGPT is an example of AI that uses ML to understand and respond to human-generated prompts.

Narrow AI and artificial general intelligence 

All types of AI are considered Narrow AI. Their scope is limited, and they’re not sentient. Examples of such AI are voice assistants, chatbots, image recognition systems, self-driving vehicles, and maintenance models.

Artificial general intelligence (AGI) is a hypothetical concept that refers to a self-aware AI that can match or even surpass human intelligence. While some experts estimate that AGI is several years or even decades away, others believe that it’s impossible.

What is generative AI? 

Generative AI refers to a subset of artificial intelligence techniques that involve the creation and generation of new content, such as images, text, audio, or even videos. It involves training models to understand patterns in existing data and then using that knowledge to generate new, original content that resembles the training data.

One popular approach to generative AI is the use of generative adversarial networks (GANs). GANs consist of two neural networks: a generator network and a discriminator network. The generator network creates new content, while the discriminator network evaluates and distinguishes between the generated content and real content. The two networks work in a competitive manner, with the generator attempting to produce content that the discriminator cannot distinguish from real data.

Generative AI has applications in various domains. For example:

  1. Image Generation: Generative AI can be used to generate realistic images, such as creating photorealistic faces, landscapes, or even entirely new objects that do not exist in the real world.

  2. Text Generation: Generative models can be trained to generate coherent and contextually relevant text, which can be used for tasks like chatbots, content creation, or language translation.

  3. Music and Audio Generation: Generative AI can create new musical compositions or generate realistic sounds and voices.

While generative AI has many positive applications, there are also concerns about its potential misuse, such as generating fake content or deepfake videos that can be used to deceive or manipulate people. Ethical considerations and responsible use of generative AI are important factors to address these risks.

In the realm of cybersecurity, generative AI can be both a tool and a challenge. It can be used for generating realistic synthetic data to train models and improve security measures, but it can also pose risks when used for malicious purposes, such as generating convincing phishing emails or deepfake social engineering attacks. It highlights the importance of developing robust defenses and detection mechanisms to mitigate potential threats.

What are the risks of AI in cyber security 

Like any technology, AI can be used for good or malicious purposes. Threat actors can use some of the same AI tools designed to help humanity to commit fraud, scams, and other cybercrimes.

Let’s explore some risks of AI in cyber security:

1: Cyber attacks optimization 

Experts say that attackers can use generative AI and large language models to scale attacks at an unseen level of speed and complexity. They may use generative AI to find fresh ways to undermine cloud complexity and take advantage of geopolitical tensions for advanced attacks. They can also optimize their ransomware and phishing attack techniques by polishing them with generative AI.

2: Automated malware 

An AI like ChatGPT is excellent at accurately crunching numbers. According to Columbia Business School professor Oded Netzer, ChatGPT can already “write code quite well.

Experts say that in the near future, it may help software developers, computer programmers, and coders or displace more of their work.

While software like ChatGPT has some protections to prevent users from creating malicious code, experts can use clever techniques to bypass it and create malware. For example, one researcher was able to find a loophole and create a nearly undetectable complex data-theft executable. The executable had the sophistication of malware created by a state-sponsored threat actor*.

This could be the tip of the iceberg. Future AI-powered tools may allow developers with entry-level programming skills to create automated malware, like an advanced malicious bot. So,what are malicious bots? A malicious bot can steal data, infect networks, and attack systems with little to no human intervention.

* security-world

3: Physical safety 

As more systems such as autonomous vehicles, manufacturing and construction equipment, and medical systems use AI, risks of artificial intelligence to physical safety can increase. For example, an AI-based true self-driving car that suffers a cyber security breach could result in risks to the physical safety of its passengers. Similarly, the dataset for maintenance tools at a construction site could be manipulated by an attacker into creating hazardous conditions.

AI privacy risks 

In what was an embarrassing bug for OpenAI CEO Sam Altman,ChatGPT leaked bits of chat history of other users. Although the bug was fixed, there are other possible privacy risks due to the vast amount of data that AI crunches. For example, a hacker who breaches an AI system could access different kinds of sensitive information.

An AI system designed for marketing, advertising, profiling, or surveillance could also threaten privacy in ways George Orwell couldn’t fathom. In some countries, AI-profiling technology is already helping states invade user privacy.

Stealing AI models 

There are some risks of AI model theft through network attacks, social engineering techniques, and vulnerability exploitation by threat actors such as state-sponsored agents, insider threats like corporate spies, and run-of-the-mill computer hackers. Stolen models can be manipulated and modified to assist attackers with different malicious activities, compounding artificial intelligence risks to society.  

Data manipulation and data poisoning 

While AI is a powerful tool, it can be vulnerable to data manipulation. After all, AI is dependent on its training data. If the data is modified or poisoned, an AI-powered tool can produce unexpected or even malicious outcomes.

In theory, an attacker could poison a training dataset with malicious data to change the model’s results. An attacker could also initiate a more subtle form of manipulation called bias injection. Such attacks can be especially harmful in industries such as healthcare, automotive, and transportation.


You don’t have to look further than cinema to see how AI-powered tools are helping filmmakers trick audiences. For example, in the documentary Roadrunner, the late celebrity chef Anthony Bourdain’s voice was controversially created with A.I.-generated audio and easily tricked viewers. Similarly, the veteran actor, Harrison Ford, was convincingly de-aged by several decades with the power of artificial intelligence in Indiana Jones and the Dial of Destiny.

An attacker doesn’t need a big Hollywood budget to pull off similar trickery. With the right footage, anyone can make deepfake footage by using free apps. People can also use free AI-powered tools to create remarkably realistic fake voices trained on mere seconds of audio.

So it should come as no surprise thatAI is now being used for virtual kidnapping scams. Jennifer DeStefano experienced a parent’s worst nightmare when her daughter called her, yelling and sobbing. Her voice was replaced by a man who threatened to drug her and abuse her unless paid a $1 million ransom.

The catch? Experts speculate the voice was generated by AI. Law enforcement believes that in addition to virtual kidnapping schemes, AI may help criminals with other types of impersonation fraud in the future, including grandfather scams.

Generative AI can also produce text in the voice of thought leaders. Cybercriminals can use this text to run different types of scams, such as fraudulent giveaways, investment opportunities, and donations on mediums like email or social media platforms like Twitter.

More sophisticated attacks 

As mentioned, threat actors can use AI to create advanced malware, impersonate others for scams, and poison AI training data. They can use AI to automate phishing, malware, and credential-stuffing attacks. AI can also help attacks evade security systems like voice recognition software in attacks called adversarial attacks.

Reputational damage 

An organization that utilizes AI can suffer reputational damage if the technology malfunctions or suffers a cyber security breach, which results in data loss. Such organizations may face fines, civil penalties, and deteriorating customer relationships.

How to protect yourself from the AI risks

While AI is a powerful tool, it can present some cyber security risks. Both individuals and organizations must take a holistic and proactive approach in order to use the technology safely.

Here are some tips that can help you mitigate the risks of AI:

1: Audit any AI systems you use 

Check the current reputation of any AI system you use to avoid security and privacy issues. Organizations should audit their systems periodically to plug vulnerabilities and reduce AI risks. Auditing can be done with the assistance of experts in cyber security and artificial intelligence who can complete penetration testing, vulnerability assessments and system reviews.

2: Limit personal information shared through automation 

More people are sharing confidential information with artificial intelligence without understanding the AI risks to privacy. For example, staff at prominent organizations were found putting sensitive company data in ChatGPT. Even a doctor submitted his patient’s name and medical condition in the chatbot to craft a letter, not appreciating the ChatGPT security risk.

Such actions pose security risks and breach privacy regulations like HIPAA. While AI language models may not be able to disclose information, conversations are recorded for quality control and are accessible to system maintenance teams. That’s why it’s best practice to avoid sharing any personal information with AI.

3: Data security 

As mentioned, AI relies on its training data to deliver good outcomes. If the data is modified or poisoned, AI can deliver unexpected and dangerous results. To protect AI from data poisoning, organizations must invest in cutting-edge encryption, access control, and backup technology. Networks should be secured with firewalls, intrusion detection systems, and sophisticated passwords.

4: Optimize software 

Follow all the best practices of software maintenance to protect yourself from the risk of AI. This includes updating your AI software and frameworks, operating systems, and apps with the latest patches and updates to reduce the risk of exploitation and malware attacks. Protect your systems with next-generation antivirus technology to stop advanced malicious threats. In addition, invest in network and application security measures to harden your defenses.

5: Adversarial Training 

Adversarial training is an AI-specific security measure that helps AI respond to attacks. The machine learning method improves the resilience of AI models by exposing them to different scenarios, data, and techniques.             

6: Staff training 

The risks of AI are quite broad. Consult with experts in cyber security and AI to train your employees in AI risk management. For example, they should learn to fact-check emails that may potentially be phishing attacks designed by AI. Likewise, they should avoid opening unsolicited software that could be malware created by artificial intelligence.

7: Vulnerability management 

Organizations can invest in AI vulnerability management to mitigate the risk of data breaches and leaks. Vulnerability management is an end-to-end process that involves identifying, analyzing, and triaging vulnerabilities and reducing your attack surface related to the unique characteristics of AI systems.

8: AI incident response 

Despite having the best security measures, your organization may suffer an AI-related cyber security attack as the risks of artificial intelligence grow. You should have a clearly outlined incident response plan that covers containment, investigation, and remediation to recover from such an event.

The flip side: How AI can benefit cyber security 

Industries of different sizes and sectors use AI to enhance cyber security. For example, all types of organizations worldwide use AI to authenticate identities, from banks to governments. And the finance and real estate industries use AI to find anomalies and reduce the risk of fraud.

Here is more on how AI benefits cyber security:

1: Cyber threat detection 

Sophisticated malware can bypass standard cyber security technology by using different evasion techniques, including code and structure modification. However, advanced antivirus software can use AI and ML to find anomalies in a potential threat’s overall structure, programming logic, and data.

AI-powered threat detection tools can protect organizations by hunting these emerging threats and improving warning and response capabilities. Moreover, AI-powered endpoint security software can shield the laptops, smartphones, and servers in an organization.

2: Predictive models 

Cybersecurity professionals can go from a reactive to a proactive posture by utilizing generative AI. For example, they can use generative AI to create predictive models that identify new threats and mitigate risks.

Such predictive models will result in:

  • Faster threat detection
  • Time savings
  • Cost reduction
  • Improved incident response
  • Better protect from risks

3: Phishing detection 

Phishing emails are a significant threat vector. With little risk, threat actors can use phishing expeditions to steal sensitive information and money. Moreover, phishing emails are becoming more challenging to differentiate from real emails.

AI can benefit cyber security by enhancing phishing protection. Email filters that utilize AI can analyze text to flag emails with suspicious patterns and block different types of spam.

4: Identifying bots 

Bots can harm or take down networks and websites, negatively impacting an organization’s security, productivity, and revenue. Bots can also take over accounts with stolen credentials and help cybercriminals engage in fraud and scams.

Software that leverages machine learning-based models can analyze network traffic and data to identify bot patterns and help cyber security experts negate them. Network professionals can also use AI to develop more secure CAPTCHA against bots.

5: Securing networks 

Attackers can exfiltrate data or infect systems with ransomware after breaching a network. Detecting such threats early is critical. AI-based anomaly detection can scan network traffic and system logs for unauthorized access, unusual code, and other suspicious patterns to prevent breaches. Moreover, AI can help segment networks by analyzing requirements and characteristics.

6: Incident response 

AI can boost threat hunting, threat management, and incident response. It can work around the clock to respond to threats and take emergency action, even when your team is offline. In addition, it can reduce incident response times to minimize harm from an attack.

7: Mitigate insider threats 

Insider threats must be taken seriously because they can cost an organization revenue, trade secrets, sensitive data, and more. There are two types of insider threats: malicious and unintentional. AI can help stop both types of insider threats by identifying risky user behavior and blocking sensitive information from leaving an organization’s networks.

8: Strengthen access control 

Many access control tools use AI to improve security. They can block logins from suspicious IP addresses, flag suspicious events, and ask users with weak passwords to change their login credentials and upgrade to multi-factor authentication.

AI also helps authenticate users. For example, it can leverage biometrics, contextual information, and user behavior data to accurately verify the identity of authorized users and mitigate the risk of misuse.

9: Identify false positives 

False positives can be exhausting for IT teams to manage. The sheer volume of false positives can result in mental health challenges. They can also force teams to miss legitimate threats. The volume of false positives can be reduced, though, with cyber security tools that use artificial intelligence to improve threat detection accuracy. Such tools can also be programmed to automatically manage low-probability threats that consume a security team’s time and resources.

10: IT staffing efficiency and costs 

Many small to medium-sized businesses can’t afford to invest in a large in-house cyber security team to manage increasingly sophisticated threats around the clock. However, they can invest in AI-powered cyber security technology that works 24/7 to offer continuous monitoring, improve efficiency and reduce costs. Such technology can also scale with the growth of a company cost-effectively.

In addition, AI improves staff efficiency because it doesn’t tire. It offers the same quality of service at all hours of the day, reducing the risk of human error. AI can also manage significantly more data than a human security team.


What are the biggest risks of AI?

What are the biggest risks of AI?

While AI offers tremendous opportunities and benefits, there are also potential risks and challenges associated with its development and deployment. Here are some of the major risks associated with AI:

  1. Bias and Discrimination: AI systems can inherit biases from the data they are trained on, which can lead to discriminatory outcomes. If the training data contains biases or reflects societal prejudices, AI systems can perpetuate and amplify those biases, leading to unfair treatment or decision-making.

  2. Privacy and Security Concerns: AI systems often require access to large amounts of data, including personal or sensitive information. There's a risk of data breaches or unauthorized access, which could compromise privacy and confidentiality. Adhering to robust security measures and privacy safeguards is crucial to mitigate these risks.

  3. Job Displacement and Economic Impact: AI automation has the potential to disrupt industries and replace certain job roles, leading to job displacement and economic challenges for those affected. It is important to consider the potential societal impact and develop strategies to mitigate these effects, such as reskilling and upskilling programs.

  4. Ethical Dilemmas: AI can raise complex ethical questions and dilemmas. For example, decisions made by AI systems, such as autonomous vehicles or medical diagnosis systems, can have life-or-death implications. Determining responsibility, accountability, and ensuring transparency in AI decision-making processes are critical aspects that need careful consideration.

  5. Adversarial Attacks and Manipulation: AI systems can be vulnerable to adversarial attacks, where malicious actors intentionally manipulate or deceive the system by introducing subtle changes to input data. This can have serious consequences in domains like cybersecurity, where AI systems may be used for intrusion detection or malware detection.

  6. Dependence and Overreliance: Overreliance on AI systems without proper understanding or human oversight can be risky. Blind trust in the decisions made by AI without critical evaluation can lead to errors or unintended consequences.

It is important to actively address these risks through responsible AI development, robust regulations, ongoing research, and collaboration between various stakeholders to ensure that AI technologies are developed and deployed in a manner that maximizes benefits while minimizing potential harm.

How is AI used in cyber security?

How is AI used in cyber security?

AI is increasingly being utilized in cyber security to enhance threat detection, incident response, and overall defense against cyber attacks. Here are several ways AI is used in cyber security:

  1. Threat Detection: AI can analyze large volumes of data, including network traffic, system logs, and user behavior, to identify patterns and anomalies that indicate potential threats. Machine learning algorithms can learn from historical data to detect known attack patterns and adapt to identify emerging threats.
  2. Intrusion Detection and Prevention: AI-powered intrusion detection systems (IDS) and intrusion prevention systems (IPS) can monitor network traffic, identify suspicious activities, and respond in real-time to prevent or mitigate attacks. AI algorithms can analyze network patterns, signatures, and behaviors to identify and block malicious activities.
  3. Malware Detection: AI techniques such as machine learning can be applied to analyze file attributes, code behavior, and network communication patterns to detect and classify malware. AI-based antivirus and anti-malware solutions can identify and block known malware as well as detect new and evolving threats.
  4. User and Entity Behavior Analytics (UEBA): AI can analyze user behavior, such as login patterns, access privileges, and data usage, to detect unusual or suspicious activities that may indicate insider threats or compromised accounts. UEBA systems use machine learning to establish baseline behavior and detect deviations from normal patterns.
  5. Security Analytics: AI enables the analysis of large-scale security data, including log files, network traffic, and security events, to identify potential threats or vulnerabilities. AI can automate the correlation of data from various sources, prioritize alerts, and provide security analysts with actionable insights.
  6. Phishing and Fraud Detection: AI can assist in detecting and preventing phishing attacks by analyzing email content, links, and sender behavior. Machine learning algorithms can learn to identify patterns and indicators of phishing emails, helping to protect users from falling victim to fraudulent activities.
  7. Cyber security Response and Automation: AI technologies, such as chatbots or virtual assistants, can help automate and streamline incident response processes. They can provide real-time guidance to security teams, assist in threat hunting, and facilitate faster incident resolution.

It's important to note that while AI enhances cyber security capabilities, it is not a silver bullet and should be complemented with other security measures, human expertise, and ongoing monitoring to address emerging threats and challenges.