From using AI in cybersecurity to automate manual tasks to enhancing third-party risk management processes, Artificial Intelligence (AI) is reshaping the cybersecurity landscape. But as we embrace this powerful technology, are we fully aware of the unseen risks it brings?

By training neural networks on vast datasets, AI models can learn to identify patterns and anomalies, making them invaluable tools in the fight against cyber threats like phishing attacks and malware. 

AI-powered solutions can analyze millions of inputs in real time, providing cybersecurity professionals with actionable threat intelligence to stay one step ahead of cybercriminals.

As cybersecurity teams increasingly rely on AI-driven solutions, they must also know the potential risks. As AI systems become more complex, their decision-making processes can become opaque, making it difficult to understand how they arrive at their conclusions.

By understanding AI’s potential risks and limitations, we can develop more robust and resilient systems that harness machine learning’s power while mitigating its drawbacks. 

The Role and Benefits of AI in Cybersecurity

AI is changing cybersecurity by automating tasks, improving threat detection, and streamlining incident response. Here are some key benefits:

Reduced Manual Tasks

  • AI automates evidence collection for compliance, saving time and increasing accuracy.
  • Process automation can check if all certificates on production servers are updated every 90 days, reporting non-conformities and reducing the risk of cyberattacks.

Improved Threat Detection and Response

  • Machine learning algorithms analyze real-time data to identify potential threats and outliers.
  • Large language models like ChatGPT provide updates on new cyberattacks, vulnerabilities, and user behavior anomalies.
  • API-driven integrations allow organizations to set threat response parameters, reducing the attack surface.
  • AI can detect deviations in endpoint activity and automatically contain threats to prevent data breaches.

Streamlined Incident Response

  • AI quickly analyzes data during incidents, enabling faster containment and resolution.
  • Deep learning models can be trained on past incidents for better response to sophisticated attacks.
  • Generative AI updates trending incidents and breaches while triggerable workflows automate containment actions.
  • AI-powered note-taking ensures all relevant data is captured and detailed reports are generated quickly.
  • Security operations centers use AI to manage incidents, perform threat hunting, and protect sensitive information.

AI in cybersecurity is crucial in combating the growing threat of cybercrime. Organizations can better protect their assets and prevent costly security breaches by leveraging AI’s power to analyze data, detect threats, and automate responses.

Common Misconceptions of Artificial Intelligence

Can AI Replace Humans?

The most common misconception about AI is that it will replace humans. That fear is not new; when personal computers started to gain traction in the 1980s and 90s, people worried about mass layoffs due to these advancements. 

While some changes happen with new technologies, which are sometimes disruptive, in the long run, new technology increases productivity and innovation. That ultimately leads to the creation of new jobs and new industries.

Is AI Unbiased and Fair? 

Another common misconception is that AI is unbiased and fair because human influence doesn’t corrupt or affect it. Ironically, an equally large group of people believe artificial intelligence is biased and unfair. 

In reality, AI is neither prejudiced nor unbiased—but it can be manipulated or influenced by the humans who design and deploy the technology and select the data it uses to learn. 

Is AI Technology Complicated, Expensive, and Intrusive?

Many people are interested in AI but believe it’s too complicated or expensive. (Or they simply don’t know where to start.) This often comes from someone taking too big of a bite into AI. 

As noted earlier, AI exists in many forms and has many uses within the cybersecurity world. Take baby steps into AI to find the right “amount” of AI and the best use cases for your business. 

The Unseen Risks of AI

As AI becomes increasingly popular in cybersecurity, it’s crucial to understand its limitations and the risks of over-reliance. While AI can bring efficiency and automation to various tasks, it’s not a silver bullet solution.

1. False Positives and False Negatives

AI systems can produce false positives and negatives, especially in the rapidly evolving threat landscape. AI may need continuous tuning and adjustments to keep up with emerging threats.

For example, an automated vulnerability patching system that misses critical new vulnerabilities due to a lack of human oversight.

2. Lack of Contextual Understanding

AI needs to gain the contextual understanding and intuition that human experts possess. It may miss subtle nuances or fail to connect the dots in complex security scenarios. 

AI in cybersecurity relies on predefined rules and algorithms that often lack context, leading to potential unintended consequences. To mitigate these unseen risks, organizations must strike a balance between automation and human oversight, ensuring that the benefits of AI outweigh the possible drawbacks.

The WannaCry outbreak in 2017 is a prime example of how automated security systems can cause unintended disruptions without proper context and understanding of the threat. Some intrusion prevention systems and firewalls responded to the ransomware by applying broad rules, inadvertently blocking legitimate network traffic and causing unnecessary collateral damage, highlighting the importance of considering the potential impact of automated actions in cybersecurity.

3. Bias and Data Quality Issues

AI systems are only as good as the data they are trained on. Biased or incomplete datasets can lead to skewed results and incorrect conclusions. For example, an AI model trained on a dataset that underrepresents certain types of cyberattacks may fail to detect those attacks in real-world scenarios.

4. Adversarial Attacks and Evasion Techniques

Cybercriminals can exploit weaknesses in AI systems using adversarial techniques. They may manipulate input data or use evasion techniques to bypass AI-based defenses. An attacker crafts malicious code that appears benign to an AI-powered malware detection system, allowing it to evade detection

5. Opacity and Lack of Explainability

Many AI systems, particularly those based on deep learning, are opaque and lack explainability. Understanding how AI models arrive at their decisions can take time, making it challenging to trust and verify their outputs. 

For example, an AI system flags a legitimate activity as malicious, but the security team cannot determine why due to the lack of transparency in the AI model.

Ethical Considerations of AI

As noted earlier, although AI is not inherently biased or unfair, humans can make AI do what they want. The output is only as accurate and reliable as the data fed into it. If you teach AI that 18+1 = 20, it will report that as the correct answer. 

Consider a typical generative AI use case: content creation. Using AI to draft a policy, report, or blog post may seem innocent. But AI can lead organizations astray if the content isn’t factual (or worse, manipulated by others). Assuring accountability and transparency on how the organization uses AI is vital to its successful use. 

Being cautious, especially with automated decision-making tools, reduces the risk of injecting biases into AI. In 2018, it was revealed that Amazon had developed a computerized system to review job applicants’ resumes and provide recommendations for hiring. However, the system exhibited gender bias, penalizing resumes that included terms associated with women.

According to a report by Reuters in 2018, Amazon’s automated system learned from resumes submitted to the company over ten years. 

Due to the historically male-dominated tech industry, most resumes used for training the system were from male applicants. As a result, the system developed a bias against resumes that contained terms commonly found in women’s resumes, such as “women’s college” or membership in women’s organizations.

The unintended consequence of this bias was that the automated system systematically downgraded resumes from female applicants, leading to gender discrimination in the recruitment process. Thankfully, Amazon recognized the bias (and the potential legal implications) and stopped using the system. But this is an excellent example of how AI can wander into unethical situations.

Best Practices for Implementing AI

Phase 1: Objectives and Use Cases

  • Identify specific tasks or processes to be automated
  • Evaluate what problems or issues to be resolved by the AI
  • Determine the desired outcome and benefits 
  • Develop a mechanism to measure the change

Phase 2: Research and Define Options

  • Evaluate your existing technology 
  • Identify gaps and necessary additions 
  • Define workflows, trigger points, and integrations
  • Assess functionality and security

RiskInsider Tip: As new AI technologies emerge, numerous open-source and free options are available. Because they are easy to obtain, these tools often bypass corporate third-party risk management processes and must be included in company-wide security controls. If an employee creates a free ChatGPT account, uploads the organization’s strategic plan, and asks ChatGPT to create a slide deck, will the organization ever know? Be sure to include frequent education and monitoring for new tools and unusual usage. 

Phase 3: Monitor and Scale

  • Assess the outputs and functionality of the AI
  • Determine the effectiveness of the AI
  • Fine-tune and adjust AI to scale and improve
  • Provide training and education related to AI

How Does Your Organization Leverage AI for Cybersecurity?

As AI continues to evolve and become more sophisticated, organizations must carefully consider how they deploy, measure, and adjust their AI usage in cybersecurity. It’s essential to strike the right balance between leveraging AI’s benefits and mitigating its potential risks. Here are some key considerations:

  • Evaluate your organization’s specific security needs and identify areas where AI can provide the most value
  • Define clear metrics and Key Performance Indicators (KPIs) to measure the effectiveness of your AI cybersecurity solution.
  • Regularly review and update AI models to adapt to new cyberattacks and vulnerabilities.
  • Encourage collaboration between cybersecurity professionals, AI experts, and other stakeholders within your organization
  • Share knowledge, best practices, and lessons learned to continuously improve your AI cybersecurity strategies.

As organizations deploy, measure, and adjust AI usage, new and engaging use cases will likely emerge, challenging the cybersecurity industry to stay on its toes. Don’t take this journey alone—read more in our blogs or request a demo of RiskOptics Solutions today.