🤖 Day 25: Explore AI-driven security testing and share potential use cases

Day 25 is all about Security Testing. If you’ve ever tried your hand at Application Security Testing, you’ll know that it’s a complex and evolving topic that usually requires in-depth technical knowledge. Various tools exist to make running security tests and audits easier, but understanding their output can be challenging. Today we want to explore whether AI can make Security Testing more accessible.

Task Steps

You have two options for today:

Option 1: If you want to evaluate an AI empowered Security Testing tool (or already use one). For this option, the tasks are:

  • Choose your tool: Research security testing tools that claim to be AI empowered and choose one to evaluate.
  • Run the tool against a target system: Timebox this activity to about 30 mins and configure and run the tool against a target system of your choice:
    • Remember, you must have permission to run security test against the target.
    • How easy was it to set up the testing?
    • Do you understand what is being tested for?
  • Review the output: Review any issues that the tool found and consider:
    • What information did the tool provide about any potential vulnerabilities found? Was it understandable?
    • Do you have an understanding of what was checked by the tool?

Option 2: You don’t want to (or can’t) install security tools. For this option the tasks are:

  • Read an introductory article on AI-driven Security Testing: Find an introductory article that discusses AI Driven Security Testing and consider its impact on Software Testing Teams
  • What are the barriers to effective Security Testing within your team? Think about security testing in your context and the current challenges and barriers to you adopting Security Testing as part of your day to day testing activities.
  • What would an AI Security Testing Tool do for your team? What could a (real or hypothetical) AI empowered tool provide that would eliminate or reduce the barriers within your team to adopting Security Testing?
  • Is Security Testing an appropriate use for AI? Based on what you have learned about AI and AI in Testing consider whether delegating Security Testing to an AI empowered tool is appropriate.

Share Your Findings

Whether you choose option 1 or 2, consider sharing your findings with the community. You might share:

  • Which option you chose.
  • Your insights from this exercise.
  • Whether you think Security Testing is an area that AI can effectively support and improve.
  • The risks and opportunities for AI supported Security Testing within your team.

Why Take Part

  • Find new ways to find important issues: Security Testing tools are notoriously difficult to learn and use effectively, and understanding the outputs often requires a high degree of domain knowledge. Taking part in this challenge allows you to explore new ways of tackling security testing that leverages AI to simplify the usage of these tools and provide more explainable outputs.

:rocket: Level up your learning experience. Go Pro!


Hello all,

I am quite new to Security Testing and I found this article insightful in understanding AI’s role in this field. I’m keen to learn more about the perspectives of experts in Security Testing on this topic.

Article: How AI Can Boost Your Security Testing Skills


Hi my fellow testers, for todays challenge I chose option 2

Read an introductory article on AI-driven Security Testing:

I researched for and read this article: How Artificial Intelligence Will Drive the Future of Penetration Testing in IT Security - Cybersecurity | Digital Forensics | Penetration Testing | ERMProtect

What are the barriers to effective Security Testing within your team?

The main barrier for me would just be not knowing even where to start in this area, I’ve dabbled a bit with the Owasp Zap tool in the past but that’s about it

What would an AI Security Testing Tool do for your team?

If an AI powered security tool was particularly user friendly & we could have confidence in its results then that would be a big plus in the consideration of its adoption

Is Security Testing an appropriate use for AI?

I would say the information gathering side is probably fine and helpful but the analysing & implementation of the results I would still leave to a human with experience in that area.


HI can you advise were day 23 is please


Hi All

Task Summary

The task for Day 25 was centered around Security Testing, with a focus on exploring the potential of AI in making Security Testing more accessible.

My Approach (Option 2)

I chose Option 2 as I couldn’t install security tools. I started by reading an introductory article on AI-driven Security Testing¹². This gave me a good understanding of how AI can potentially revolutionise Security Testing.

Next, I reflected on the barriers to effective Security Testing within my team. I identified a lack of in-depth technical knowledge and the complexity of existing tools as major challenges⁶⁷.

I then pondered on what an AI Security Testing Tool could do for my team. I realised that an AI-empowered tool could potentially simplify the usage of these tools and provide more explainable outputs, thereby reducing the barriers to adopting Security Testing³¹¹².

Finally, I considered whether delegating Security Testing to an AI-empowered tool is appropriate. Based on what I learned about AI and AI in Testing, I concluded that AI can effectively support and improve Security Testing, provided it is used responsibly and ethically¹¹¹⁷.

In conclusion, I found this exercise insightful and it made me appreciate the potential of AI in making Security Testing more accessible. I am sharing my findings with the community, highlighting the opportunities and risks associated with AI-supported Security Testing within my team. I believe that AI can play a significant role in finding important issues in Security Testing, given its ability to simplify complex tasks and provide understandable outputs.

AI-Driven Security Testing Tools

Here are some AI-driven security testing tools that I found during my research:

  1. GitHub Advanced Security: This tool uses AI to help developers secure their code more efficiently¹³.
  2. Darktrace: Darktrace uses AI and machine learning to detect and respond to cyber threats in real time³.
  3. Pentest Copilot: Pentest Copilot is a powerful AI tool for enhancing and simplifying security tasks in pentesting engagements¹⁴.
  4. SecGPT: SecGPT uses AI to analyze cybersecurity reports and provide insights¹⁴.
  5. SecureGPT: SecureGPT is a free platform for security testing OpenAI ChatGPT plugins¹⁴.
  6. Page Canary: Page Canary is an AI-powered QA tester for websites¹⁴.
  7. Codiga: Codiga is another AI tool for security testing¹⁴.

These tools can help teams tackle security testing more effectively by leveraging the power of AI.

(1) Impact of AI on Test Teams | Appvance. Impact of AI on Test Teams | Appvance.
(2) AI in Software Testing | Enhancing Quality and Efficiency. https://testgrid.io/blog/ai-in-software-testing/.
(3) A Deep Dive into Security Testing: Best Practices and Approaches. A Deep Dive into Security Testing: Best Practices and Approaches | Reintech media.
(4) Four Steps to Build an Effective Security Testing Plan - Cymulate. Four Steps to Build an Effective Security Testing Plan.
(5) AI in Security Testing: Opportunities & Benefits — aqua cloud. AI in Security Testing: Opportunities & Benefits — aqua cloud.
(6) AI in Security Testing: Challenges You Can Solve & Benefits. https://amzur.com/blog/role-of-ai-in-security-testing/.
(7) 8 Benefits of Using AI in Software Testing | PractiTest. 8 Benefits of Using AI in Software Testing | PractiTest.
(8) AI in security testing: Building trust with AI - Sogeti, provider of … AI in security testing: Building trust with AI.
(9) AI: How It Is Changing Application Security Testing - Kiuwan. AI: How It Is Changing Application Security Testing - Kiuwan.
(10) Introducing AI-powered application security testing with GitHub … https://github.blog/2023-11-08-ai-powered-appsec/.
(11) 70 Best Security testing AI tools - 2024. https://topai.tools/s/security-testing.
(12) The Impact of AI on Software Testing - DZone. The Impact of AI on Software Testing - DZone.
(13) Leveraging AI for Enhanced Quality Assurance and Test Accuracy in Software. Leveraging AI for Enhanced Quality Assurance and Test Accuracy in Software | LambdaTest.
(14) Breaking Barriers: NIST Penetration Testing and the Power of … - Sxipher. https://www.sxipher.com/post/breaking-barriers-nist-penetration-testing-and-the-power-of-proactive-security.
(15) Review on barriers to online and on-screen assessment published. Review on barriers to online and on-screen assessment published - GOV.UK.
(16) 70 Best Ai driven security solution AI tools - 2024. https://topai.tools/s/AI-driven-security-solution.
(17) OWASP AI Security and Privacy Guide | OWASP Foundation. OWASP AI Security and Privacy Guide | OWASP Foundation.

Thank you


For today’s task, I was interested in what tools were available.

I started by asking Bing Copilot for suggestions. This gave a good starting point. I looked at Wiz and then overview of other tools. It is difficult to value in detail without actually testing these tools, but in principle, I get the impression that AI is being used to provide better linking of alerts to provide a better view of a threat and optimised solution suggestions.

I then spent some time on GitHub, where there is a good description of using LLMs to provide good security suggestions for developers Introducing AI-powered application security testing with GitHub Advanced Security - The GitHub Blog (just spotted that @manojk has already posted this :grinning:)

Also on the GitHubb site - I just came across the following article and thought it was worth sharing, even though it’s not directly relevant to today’s challenge. It nicely summaries a lot of the points that we have been exploring in the past few days, about coding, writing test code and using AI: Hard and soft skills for developers coding in the age of AI - The GitHub Blog


Looks like it was at

I followed option 2:

Read an introductory article on AI-driven Security Testing:
I checked out three blogs:

What are the barriers to effective Security Testing within your team?
A security tester is a profession, not an extra skill. The occupation has to do with different kind of aspects in our digital world, that is why it is a profession. So the team should not have a security test, the organization must have (internal or external is not the case). As team we can look for coverage of API OWASP top 10 during our testing and with Application Security Testing (SAST, DAST and/or IAST during development and in the CI/CD pipeline)

What would an AI Security Testing Tool do for your team?
It will take away the in-depth knowledge away from all kinds of security related use case and assist you as a tester. What the tester will get out of this knowledge about the tool, with the side effect that some domain knowledge about security will be gained

Is Security Testing an appropriate use for AI?
Yes it is in several ways:

  • Automating Repetitive Tasks:
    AI-driven tools can continuously scan code, applications, and network traffic for vulnerabilities without manual intervention.
  • Scalability and Efficiency:
    AI enables the testing of large and complex systems at scale, reducing time and resource constraints.
  • Advanced Threat Detection:
    Machine learning models can identify patterns and anomalies that might escape conventional rule-based systems.

Hi all,
So this one is nicely timed as I have no experience of security testing, however my company is on a big company push.

I had a look through everyone else’s replies.
Although my interest is in coding tools, I found one of @adrianjr links of interest.
Thanks Adrian.

Barriers for us as business, never mind within the team would be
Lack of experience in the field, very late to the game.
No centralised QA to drive this type of test. We are in delivery teams which leads to a silo mentality.
Legacy systems in Delphi, VB and older versions of .Net. We do have apps in .Net 6, but not in my current team

What would an AI tool do for us ?
I think, given the proper training on the tool, it would help us catch up.
In terms of Client awareness, I have often argued that putting things like Security Testing levels on your website not only puts current clients at ease, but can be a deciding factor in a potential Client taking our products.

Is Security Testing an appropriate use for AI:
Yes as long as you do your proper due diligence.
I would go further that you need at least 1 specialist in your company.
Security should not be a secondary role.

Did anyone see this from Bernard Marr posted on LinkedIn ?

Worried AI Will Take Your Job? How To Stay Relevant In The GenAI Era (forbes.com)


When I think of AI and Security, my first thought is “IP”: How can I protect our intellectual property if I need to share environment information to an AI tool?
My second thought is “Trust”: Can I trust an AI with the most important aspect of our applications?

Security testing in my organization includes Abuse Cases (based on Threat Model) or Penetration Test (for application and infrastructure).
I could see AI being used for creating threats (i.e. simulating an attacker) by trying various techniques it learned.
But: Asking ChatGPT whether it can help me with that, it refused (for good reason):
Are you allowed to give me tips on breaking cyber security?
I cannot assist you with that. My purpose is to provide helpful and ethical information. Engaging in activities that involve breaking cybersecurity, hacking, or any other illegal activities is against the law and goes against ethical principles. It’s important to respect the privacy and security of individuals and organizations. If you’re interested in cybersecurity, there are plenty of legitimate ways to learn about it, such as taking courses, obtaining certifications, and practicing in ethical hacking environments. If you have any other questions about cybersecurity or related topics, feel free to ask!


Option 1: Doing part of Option 1

Here are some AI powered security tools.
Need to find a way to run an application outside company application and company computer :slight_smile:

  1. Snyk Code: Snyk Code is an AI-powered static application security testing (SAST) tool that helps developers find and fix vulnerabilities in their code early in the development process. It uses ML algorithms to detect security issues in the codebase, including known vulnerabilities, insecure patterns, and potential exploits.
  2. Contrast Security: Contrast Security offers a suite of security tools that leverage AI and ML for application security testing. Contrast Assess is their interactive application security testing (IAST) tool that uses AI to analyze application behavior and identify vulnerabilities in real-time as the application runs.
  3. Fortify SCA (Software Security Center): Fortify, now part of Micro Focus, provides Static Code Analyzer (SCA) tools that utilize AI and ML to improve the accuracy of identifying vulnerabilities in code. Fortify’s SCA can detect security weaknesses, coding errors, and compliance issues in applications.
  4. Checkmarx CxSAST: Checkmarx is a leading provider of Static Application Security Testing (SAST) tools. Checkmarx CxSAST incorporates AI-driven technology to detect and prioritize security vulnerabilities in the source code of applications.
  5. Netsparker: Netsparker is a web application security scanner that employs AI to automate the process of identifying and scanning websites for security vulnerabilities. It helps organizations detect vulnerabilities such as SQL Injection, Cross-Site Scripting (XSS), and others.

Option 2:

1What is AI in security testing?

AI in security testing is the application of machine learning, natural language processing, computer vision, and other AI techniques to improve the quality, efficiency, and effectiveness of security testing. AI can be used to perform tasks such as fuzzing, which involves generating random or malformed inputs to test the robustness and resilience of software systems and applications. Additionally, AI can be used for penetration testing, which simulates cyberattacks to discover and exploit security weaknesses in networks, systems, and applications. Furthermore, AI can be utilized for code analysis, which involves reviewing and verifying the source code or binary code of software systems and applications for security flaws and vulnerabilities. Lastly, AI can be used for threat intelligence, which entails collecting, analyzing, and sharing information about current and emerging cyber threats and risks.

  1. Why use AI in security testing?
    AI in security testing can offer several advantages for security testers and organizations, such as speed, scale, accuracy, adaptability, and innovation. AI can perform security testing faster and more efficiently than human testers, saving time and resources. Additionally, AI can handle large and complex systems that may be difficult or impossible for human testers to cover, increasing the coverage and depth of security testing. Furthermore, AI can reduce human errors and biases, and provide more consistent and reliable results and recommendations. Moreover, AI can learn from data and feedback to adjust its strategies and techniques to cope with changing environments. Lastly, AI can discover new vulnerabilities and attacks that human testers may miss, as well as generate novel solutions and countermeasures.

3How to use AI in security testing?

AI in security testing can be used in a variety of ways to meet the goals, needs, and capabilities of security testers and organizations. For instance, AI tools and platforms can be integrated with existing security testing tools and processes, or used as standalone solutions. Security testers can also develop their own AI models and algorithms to customize and optimize their security testing processes. Additionally, they can collaborate with AI experts, such as data scientists and machine learning engineers, to leverage their expertise in applying AI to security testing. This could involve consulting with AI experts to select the best AI techniques for their security testing scenarios or to evaluate and improve the performance of their AI models.

4What are the challenges of using AI in security testing?

AI in security testing brings with it a host of potential pitfalls and limitations. Security testers and organizations need to be aware of the issues related to data quality and availability, ethical and legal issues, and trust and confidence. Data may be scarce, incomplete, inaccurate, outdated, or biased, making it difficult to obtain and maintain quality data for security testing. Moreover, AI in security testing can raise ethical and legal issues such as privacy, consent, accountability, transparency, and fairness. Lastly, it can affect the trust and confidence of security testers and other stakeholders if they do not understand how AI works or why it makes certain decisions. Therefore, security testers must ensure that they use AI in security testing responsibly and ethically while complying with applicable laws and regulations.

5How to learn more about AI in security testing?

AI in security testing is an ever-expanding field, and security testers and organizations need to stay up-to-date with the latest trends and innovations. To learn more about AI in security testing, one can read books, articles, blogs, and podcasts for insights, tips, best practices, and case studies. Courses, workshops, and webinars can also offer theoretical and practical training on how to use AI in security testing. Joining communities and networks of security testers and AI experts can foster collaboration and exchange of ideas regarding AI in security testing.

What are the barriers to effective Security Testing within your team? : Resource Constraints and Time Pressure

What would an AI Security Testing Tool do for your team? : It would make testing more available one click away.

Is Security Testing an appropriate use for AI? : Unsure on how reliable is AI security testing tool as AI only knows what is fed to it.


Day 25

Find an article about AI augmented security testing

I chose this by GitHub:

GitHub host a lot of code obviously and would have access to a lot of training data for a security AI model, so I thought they may have some interesting capabilities and insights.

It starts well, saying that we shouldn’t try and inspect security into our applications, but get that feedback as soon as possible. It mentions four features they have worked on:

  • CodeQL Auto suggestions and fixes - this looks very interesting, recommending security fixes such as rate limiting as shown in the little video on the page. It gives a fairly basic example, do a HTTP GET and then do a direct SQL query, which is definitely a security problem. One would hope that organisations would be able to hit that minimum, or maybe experience teaches us otherwise!
  • Passwords in source control detection - raises a good point, as secrets are often unstructured and hard to detect, then an LLM could learn to detect them effectively. I suppose its easier to detect the fields which are literally called passwords, but more subtle secrets, API keys for example, which may just look like guids used in tests for example.
  • Regular Expression Custom Patterns for Secrets - this is nice, you can add patterns to detect for within your codebase. You can generate with AI with a simple form for descriptions and examples, which is a decent way to build a good prompt, with a selection of results to use. I would be very interested to test this, as the road to hell is paved with regular expressions.
  • Dashboard - Showing risk, remediation and prevention across multiple projects.

Like many scanners (even AI augmented ones) can give the illusion of making a difference to a non functional area but can be deceptive. Recommending rate limiting in the auto suggestions is a distant second to having a sensible architecture! I fear that teams might be tempted by scanners detecting problems they don’t understand and then auto fixes that might be wrong that they don’t understand. The password work is good though and a nice baseline with fairly universal understanding. Also a starter dashboard across multiple projects is a nice touch, although as an invitation to test better, rather than a goal to hit.


Hi there :wave:

Found on this article and it talks about benefits of bring AI in security testing. Security Audit and triaging will be more easier. Getting trained with new vulnerabilities is a challenge 'm seeing



Is it you writing this or you are using GPT / LLM to summarize your answers?

I see text like Security Testing³[^10^]¹²

What does this mean?

Hello @billmatthews and fellow learners,

I chose option #2 as I am not much into security testing for my day-to-day work.

I read the Shift Left Using AI-powered App Sec article from Github Blog on this topic.

Here is a summary of my notes:

Here is my video blog explaining all these aspects of AI for Security Testing:

Do share your feedback, thanks!


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Hi Rahul, yeah that would have been added while copying from the copilot. Its just reference to the below, 3,10,12. I format the answers using gpts.

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Based on my experience with security testing tools and security testing, I choose Option 2 for today’s task.

1. Reading an Introductory Article on AI-Driven Security Testing

Article 1: Artificial Intelligence in Security Testing: Building Trust with AI



Artificial Intelligence (AI) is revolutionizing security testing through automation technology and advanced detection methods.

  • :zap: Automation: With the power of AI, existing tools can automatically discover and respond to security threats, greatly improving response times.

  • :fire: Advanced Detection: Machine learning technologies enable us to identify potential danger patterns and abnormal behaviors, preventing security incidents in advance.

  • :globe_with_meridians: Widespread Application of AI: In the field of cybersecurity, AI’s applications are extensive, including User and Entity Behavior Analytics (UEBA), honey pots (used to deceive and capture hackers), and deep learning-based solutions, all of which greatly enhance our ability to identify and defend against network threats.


This article discusses the application of Artificial Intelligence (AI) in security testing, emphasizing the potential of AI in enhancing network security and efficiency. It describes the evolution of malware and cybersecurity challenges, as well as the necessity of employing automation and AI to address the increasing scale and complexity of threats. The article also introduces AI’s applications in early network security, such as detecting polymorphic viruses and using machine learning for pattern recognition, as well as AI’s role in enhancing human expertise and addressing the shortage of cybersecurity talent. Finally, it discusses the dual advancement of AI in network attack and defense.

Article 2: ChatGPT AI in Security Testing: Opportunities and Challenges



ChatGPT AI can automate security testing tasks, significantly improving work accuracy and efficiency.

  • :arrows_counterclockwise: Automated Security Monitoring: ChatGPT AI can automatically conduct vulnerability scans, deep penetration testing, log data analysis, and detect potential intrusion behaviors, making security detection more efficient and intelligent.

  • :white_check_mark: Precise and Efficient Risk Identification: Through in-depth analysis, ChatGPT AI can provide detailed security vulnerability reports, helping to quickly locate and discover new security risk points, ensuring system stability.


CYFIRMA’s article discusses the opportunities and challenges of using ChatGPT AI in security testing. The article emphasizes the potential of ChatGPT in automating security tasks (such as vulnerability scanning and penetration testing), as well as its ability to identify new vulnerabilities by analyzing big data and simulating real attack scenarios. However, it also highlights some challenges, including the need for large amounts of training data, the difficulty of identifying new threats, and ethical considerations. The article concludes that despite the challenges, ChatGPT still offers significant benefits in security testing.

Article 3: ChatGPT AI in Security Testing: Opportunities and Challenges



Generative Artificial Intelligence (AI) is sparking a revolution in software security testing.

  • :zap: In the testing phase, the application of generative AI helps rapidly construct potential abuse scenarios, greatly improving the efficiency of testing.

  • :page_facing_up: Real case analysis: Taking the login page as an example, the article explores how generative AI creates specific abuse cases.

  • :white_check_mark: Verification work requires validation of the abuse cases proposed by AI to ensure their relevance and accuracy.

  • :star: Conclusion: Generative AI is fundamentally changing the way Quality Assurance (QA) teams handle abuse case testing.


This article discusses methods to accelerate software security testing using generative AI. It emphasizes the potential of generative AI models in assisting QA teams in creating and executing abuse case tests. By automatically generating a large number of potential abuse scenarios, QA teams can test more quickly and achieve more comprehensive test coverage. The article also mentions instances of effectively using generative AI to generate abuse cases and emphasizes the importance of verification when using AI outputs. In summary, generative AI technology is expected to fundamentally change the way QA teams handle abuse case testing, ensuring that software is not only functionally complete but also maintains strong security in a constantly changing threat environment.

2. Reflecting on the Obstacles to Effective Security Testing in Your Team

Based on my experience with various projects, I’ve found that effective security testing in agile development teams faces several obstacles, primarily including:

  1. Time Constraints: Agile development cycles are short and emphasize rapid delivery, which may lead to security testing being seen as a secondary task because the team may be more focused on developing new features rather than ensuring security.

  2. Limited Resources: Effective security testing requires specialized knowledge and dedicated tools. In resource-constrained situations, it may be challenging to obtain these experts or tools, especially in small or medium-sized enterprises.

  3. Lack of Knowledge: Not all developers have the knowledge and experience of security testing. Agile teams may lack sufficient security awareness or security development training, which may lead to unidentified security vulnerabilities in the code.

  4. Cultural Barriers: Agile development culture may overly emphasize speed and flexibility, overlooking security. Integrating security into the agile process requires cultural change to ensure team members recognize the importance of security and incorporate it into their daily work.

  5. Integration Difficulty: Effectively integrating security testing tools and practices into the agile development process may be challenging. Finding a balance between not disrupting agile development’s rapid iteration while ensuring necessary security testing is performed is crucial.

  6. Insufficient Automation: Automation is a key part of agile development, but not all security testing can be easily automated. Lack of automation may result in repetitive manual testing work, increasing time and cost.

  7. Feedback Loop: Agile development relies on quick feedback loops. If the feedback from security testing cannot be integrated into the development process promptly, there may be missed opportunities to fix security issues, or security vulnerabilities may only be discovered after the product is released.

3. Considering the Benefits of AI Security Testing Tools for Your Team

Using AI security testing tools can certainly bring many benefits to the team, especially in accelerating the discovery and remediation of security vulnerabilities, improving the efficiency and effectiveness of security testing. Here are some specific benefits:

  1. Enhanced Detection Capability: AI security testing tools can identify and analyze complex security threats, including those that traditional tools may struggle to detect. By learning and adapting to the latest security threat features, AI tools can continuously improve detection rates.

  2. Automation and Intelligence: AI tools can automate many tedious and complex security testing tasks, such as dynamic analysis and static code analysis, freeing up the security team’s time to focus on higher-level security strategies and decisions.

  3. Real-time Monitoring and Response: AI security tools can provide 24/7 real-time monitoring and immediate response capabilities when potential threats are detected. This real-time response capability helps quickly mitigate or prevent damage from security vulnerabilities.

  4. Reduced False Positives: Through learning and optimization, AI tools can more accurately identify genuine threats, reducing the number of false positives. Reducing false positives helps security teams allocate resources more effectively and ensures attention to genuine security issues.

  5. Personalization and Adaptability: AI security testing tools can adjust based on the specific characteristics and behavioral patterns of the application, providing more personalized security testing. This adaptability means that security testing can evolve along with the application’s development.

  6. Improved Development Efficiency: Integrating AI security testing tools into the continuous integration/continuous deployment (CI/CD) process can help development teams identify and fix security vulnerabilities in the early stages of development, avoiding large-scale modifications later in development and improving overall development efficiency.

  7. Knowledge Base and Learning Ability: AI tools can learn not only from external threat intelligence but also from their own testing history, continually expanding their knowledge base. This makes each test more accurate than the last, helping the team build robust security defenses.

4. Considering Whether Using AI for Security Testing is Appropriate

Using AI for security testing is appropriate in many cases, especially when dealing with large amounts of data, quickly identifying complex threat patterns, and improving the efficiency and effectiveness of security testing. The application of AI technology can significantly enhance the capabilities of security testing, but its applicability and limitations need to be considered in specific contexts, especially concerning data security and privacy issues related to AI tools.

Hi, everyone,

for today challenge I choose option 2 in order to known more about AI Driven Security Testing, because I am not familiar with it, but it was very useful to deep me knowledge in this area.

Read an introductory article on AI-driven Security Testing

I read those articles:

AI in Security Testing: Opportunities & Benefits — aqua cloud (aqua-cloud.io)

Enhancing Security Testing with Artificial Intelligence: A Path to Resilient Systems | by Mathewsxavier | Medium

AI-Driven Security Testing: Proactive Vulnerability Detection (geekpedia.com)

Whether you think Security Testing is an area that AI can effectively support and improve.

AI can by helpful tools in security testing for data analyzing, prevention, prediction potential threats. and covering parts, that can by missing using manual testing, also reducing errors. There are mention several area, where and how AI can perform and assist in security testing:

Vulnerability Detection: AI helps you find potential weaknesses in software systems, scanning code for vulnerabilities you might miss in traditional testing.

Behavioural Analysis: With AI, you can observe and analyse system behaviours to detect anomalies or suspicious activities that could signal a security threat.

Historical Analysis: AI supports you in predicting potential threats by analysing historical data, enabling proactive measures to prevent security breaches.

Security Protocols Adaptation: AI assists in adapting security measures based on evolving threats, continuously learning and improving defence mechanisms for better protection.

Key benefits of AI-based security testing:

Enhanced Threat Detection
Reduced False Positives
Advanced Adaptive Security Measures
Improved Focus
Faster Incident Response
Automation of repetitive tasks
Advanced Analytics, for e.g. logs, network traffics

in addition to the advantages that AI tools can give to the team, it is also worth paying attention to the potential threats and risks.

Threats of AI in security testing

Overreliance on AI
Vulnerability to Adversarial Attacks
Data Bias and Privacy Concerns
Initial Algorithm Complexity
Potential Resource Intensiveness

So, it is necessary to assess where AI can be applied and use its potential, but at that time need to evaluate possible threats.

1 Like

Hola Everyone!
I have never performed ‘Security Testing’ as such so I have opted for option 2 but after reading this article:https://amzur.com/blog/role-of-ai-in-security-testing/ , here are my insights about the topic.
"To perform in-depth ‘Security Testing’ requires testing teams to be well aware of known as well as evolving security holes in all software components being using their application – be it databases, web or app servers, APIs, and so on. This is where AI comes into play when performing Security Testing’.

  • I think yes security testing is an area that AI can effectively support and improve since AI can read ‘Hackers’ patterns discover new possibilities, and test the application with large data sets will be more efficient and time-saving by humans indivisually.
  • But before actually using ‘AI’ for security testing, the risks and opportunities for AI-supported Security Testing have to be well-read and understood.