AI in Cyber Security: Pros and Cons, and What it Means for Your Business

Artificial intelligence (AI) has been making waves in virtually every industry, and cyber security is no exception. But as with any new technology, there are pros and cons to using AI in cyber security. In this blog, we’ll take a closer look at the benefits and drawbacks of AI and what it means for your business.

• The Benefits of AI in Cyber Security
• The Risks of Relying on AI in Cyber Security
• How Can AI be Used in Cyber Security?
• Creating an AI Strategy
• Best Practices for Using AI in Cyber Security

The Benefits of AI in Cyber Security

AI has become a powerful tool in the fight against cyber threats. AI-driven cyber security solutions can detect, analyze, and respond to malicious attacks faster.

Some benefits of AI in cybersecurity. Image source: OrangeMantra.com

Faster Threat Detection and Response

By leveraging AI, you can better understand your networks and identify potential threats faster than ever. AI-powered solutions can sift through vast amounts of data to identify abnormal behavior and quickly detect malicious activity, such as a new zero-day attack.

AI can also automate many security processes, such as patch management, making it easier for you to stay on top of your cyber security needs. It can help you respond faster to attacks by automating certain tasks, such as rerouting traffic away from a vulnerable server or alerting your IT team to potential issues.

Improved Accuracy and Efficiency

AI-based cyber security systems provide improved accuracy and efficiency compared to traditional security solutions. For example, AI can scan scads of devices for potential vulnerabilities in a fraction of the time it would take human operators to do the same task.

Furthermore, AI algorithms can recognize patterns that may be difficult for the human eye to spot, leading to more accurate detection of malicious activity.

Greater Scalability and Cost Savings

AI can automate tedious security tasks, freeing valuable resources to focus on other business areas.

Additionally, it can process vast amounts of data quickly and accurately to identify threats faster than any human could. This helps reduce response times to security incidents and helps lower the cost of defending against cyber threats.

AI-driven tools can also help you identify malicious activity by correlating different data points, allowing you to protect your systems proactively. These solutions are easily scalable, meaning you can purchase additional protection without significant hardware or personnel costs.

The Risks of Relying on AI in Cyber Security

AI’s ability to analyze large data sets with lightning speed promises unparalleled protection against cyber attacks, and companies worldwide are investing heavily in its application. But even as AI is increasingly relied upon to bolster security, there are still risks involved in relying on this technology.

“Cybersecurity vulnerabilities” tops the list of concerns about various types of AI risks in a study by Deloitte.

Bias and Discrimination in Decision-Making

Biased decision-making in AI systems can arise from various sources, including data sets containing biased information or algorithms that lack the necessary objectivity. If not managed properly, these biases can lead to discriminatory decisions against certain groups or individuals and have significant consequences for the organization.

For example, a decision made by an AI system based on biased inputs could lead to false positives and block legitimate users from accessing company systems, resulting in lost productivity or customers.

Lack of Explainability and Transparency

The algorithms used to make decisions about security threats are not always transparent, leaving you vulnerable to potential bias or manipulation.  AI can be difficult to interpret, making it hard to understand why decisions were made or how they can be improved.

This lack of understanding can lead to poor decisions, which can have severe implications for the security of an organization.  AI-based cyber security solutions may not always accurately identify every threat or potential breach, leading to potential risks going unnoticed and causing further damage.

Potential for Misuse or Abuse

The good guys aren’t the only ones that can benefit from this technology. AI algorithms can be designed to search through data and detect patterns quickly, making them an attractive target for malicious actors who could use them to gain access to sensitive information or attack infrastructure.

Examples of AI in Cyber Crime

Cyber criminals may use AI to:

• Easily create new malware that can contain new zero-day vulnerabilities or bypass detection.
• Create new, sophisticated, original, or targeted phishing attacks. This can increase the number of scenarios, making it difficult for reputation engines to keep up.
• Analyze and collect data much quicker and help identify other avenues of attack.
• Create deepfakes (video or audio) that can be used to convince victims in social engineering attacks.
• Conduct attacks such as intrusions or generate new hacking tools.

And because AI relies on data sets that are often biased or incomplete, it can lead to missed threats and false positives. This can create a false sense of security and lead to real-world consequences.

Finding the Right Balance: How Can AI be Used Cyber Security?

While AI can provide tremendous value, it’s crucial to ensure that organizations use it responsibly and effectively. For example, humans can automate repetitive tasks such as report generation or data processing, but nothing still compares to human connection when it comes to tasks such as customizing your services for each client’s needs. 

Key factors to consider when implementing AI solutions

When building your own AI solution, there are several key factors that you should take into consideration:

1. Data Quality: The quality of your data plays a critical role in the performance of your AI solution. Make sure to have a clean and well-annotated dataset to train your model.
2. Model Selection: Selecting the right model for your problem and having exemplary architecture is crucial. It will depend on the problem you’re trying to solve, the amount of data you have, and the desired level of accuracy.
3. Hardware: Consider the computational requirements of your AI solution and ensure that you have the necessary hardware resources to support it.
4. Explainability: As the AI model becomes more complex, it’s harder to understand how it arrived at certain decisions. It’s vital to consider the explainability of the model, especially in sensitive fields like healthcare or finance.
5. Security and Privacy: Make sure to consider the security and privacy of the data used to train and deploy the model, as well as the security of the system itself.
6. Scalability: Consider how your AI solution will scale as the volume of data or the number of users increases.
7. Ethical Implications: Consider the ethical implications of your AI solution, such as bias and fairness.
8. Integration: Consider how your AI solution will integrate with existing systems and processes.
9. Maintenance: AI systems require regular maintenance and updates, so consider how you will keep your system updated and running smoothly over time.
10. Monitoring: Consider how you will monitor the performance of your AI solution and how you will troubleshoot and fix issues if they arise.

Creating an AI Strategy that Works for Your Organization

Once you have defined your AI strategy, think about how and where to implement it best. Different organizations have different needs regarding cyber security, so there’s no one-size-fits-all when it comes to this.

However, here are several examples of how AI can be used to train and help users with cyber security best practices: 

1. Organizations can implement chatbots that can be used to provide users with information and guidance on how to protect their devices and personal information from cyber threats and answer questions about internal policies and procedures. This can offer a fast and immediate response without opening tickets at the help desk or waiting for a human response.
2. For product vendors, or when deploying new equipment to users, virtual assistants can provide step-by-step instructions on how to set up and use security features on their devices, such as firewalls and antivirus software, security and privacy configurations, sharing permissions, etc.
3. Cyber security awareness training programs can be used to educate users about common cyber threats and how to avoid them through interactive simulations and quizzes. Additionally, machine learning can be done by leveraging the results of evaluations, quizzes, surveys, cyber games and challenges, and phishing simulations.

Related reading: How Artificial Intelligence Benefits Cyber Security Awareness Training

1. Detection and prevention technologies (IPS/IDS) and user behavior analysis (UBA) can monitor and alert users about any suspicious activity on their network or device. It can also detect risky behaviors and actions and provide real-time feedback and guidance on handling potential threats. Admins can use this information to update their security perimeter devices to prevent future attacks or minimize the impact of a breach.
2. AI can be used to create new content based on live data or requests from users providing immediate and up-to-date content.

When creating an implementation plan, consider the potential risks associated with AI-powered security tools and processes. Analyze your current infrastructure to determine where AI can be used to make improvements and learn about what types of data you have available for analysis.

Finally, it’s necessary to measure the effectiveness of your AI security strategy on an ongoing basis. This will allow you to determine if your strategy works as intended and make adjustments as needed.

Best Practices for Using AI in Cyber Security

AI has the potential to revolutionize the field of cyber security. AI-powered technologies can detect anomalies, scan for vulnerabilities and malicious activity, and recognize patterns and behaviors that could indicate a threat.

By leveraging AI’s capabilities and best practices, you can improve your cyber security posture and gain a competitive advantage in the ever-evolving cyber threat landscape.

Consider implementing the following best practices when using AI in cyber security:

1. Establish Clear Goals: Have clear objectives for using AI in their security efforts. This will ensure that your resources are used effectively and that the technology is implemented in a way that aligns with their security objectives.
2. Use Accurate Data: Use high-quality data to train AI models and verify any results generated by the system. Poor quality data can lead to incorrect results, including false positives or negatives, impacting your system’s overall accuracy.
3. Utilize Multiple AI Algorithms: Use multiple AI algorithms to detect potential security threats, giving you a better chance at spotting anomalies or malicious activity. This will allow you to accurately identify and respond to threats promptly.
4. Monitor Results: Regularly monitor the performance of their AI systems to ensure they are performing as expected and delivering accurate results. This will help identify any potential problems or areas for improvement.

By following these best practices, you can fully leverage the power of AI to improve your cyber security posture and protect against malicious activity.

Combine AI with the Human Factor

Staying ahead of cyber security threats can seem daunting. With the ever-evolving nature of data breaches, organizations must understand that only one approach is no longer enough. Combining AI with the human factor is key to staying secure and compliant in a digital world.