Every modern application needs more than functional testing. A website may work perfectly, an API may return the expected response, and an automation suite may pass every test, but does that mean the application is secure?
Not necessarily.
Security weaknesses can exist in authentication, access control, input validation, session management, API integrations, data handling and application configuration. If these weaknesses are not identified early, they can expose sensitive information, disrupt business operations and damage customer trust.
Artificial intelligence is introducing new ways for QA professionals and security testers to examine applications. With carefully designed prompts, AI tools can help review code, generate security test scenarios, analyse API behaviour, identify potential weaknesses and organise vulnerability findings.
This approach is often described as AI-assisted security testing. It does not replace established security testing methods or experienced security professionals. Instead, it can make certain analysis and documentation activities more efficient.
In this guide, we explore how smart prompts can support application security testing, with practical examples for web applications, APIs and source code. We also discuss the skills QA professionals need to work with AI-assisted security testing in modern software development environments.
1. What Is AI Security Testing?
AI security testing involves using artificial intelligence to support the identification, assessment and analysis of potential security weaknesses in software applications and systems.
Traditional security testing uses a combination of manual reviews, automated scanners, test scripts, penetration testing techniques and security standards. AI adds another layer by allowing testers to describe requirements in natural language and receive structured suggestions, explanations and draft test cases.
For example, a QA professional reviewing a customer registration feature might ask an AI tool to:
- Identify potential security risks in the registration workflow.
- Generate test scenarios for input validation.
- Review whether sensitive information is exposed in error messages.
- Suggest checks for authentication and access control.
- Review code for potentially unsafe input handling.
- Organise findings into a security testing checklist.
The AI output provides a starting point for investigation. The tester must validate the suggestions against the application's design, approved requirements and actual test results.
2. Why Security Testing Is Important in Modern Software Development
Applications increasingly depend on APIs, cloud services, third-party integrations, databases, authentication providers and distributed systems. Each component introduces potential security considerations.
A security weakness in one part of an application can affect other connected components. For example, an incorrectly configured API permission may expose customer information even when the website's user interface appears to work correctly.
Security testing helps teams examine areas such as:
- Authentication: Whether users are properly verified before accessing an account.
- Authorisation: Whether users can access only the information and functions permitted by their roles.
- Input validation: Whether the application safely handles unexpected or invalid input.
- Session management: Whether sessions, tokens and authentication states are managed securely.
- Data protection: Whether sensitive information is protected during storage and transmission.
- API security: Whether endpoints enforce appropriate authentication, authorisation and data validation.
- Error handling: Whether application errors reveal unnecessary technical or sensitive information.
- Configuration: Whether security settings and deployment configurations follow appropriate standards.
Security should be considered throughout the software development lifecycle rather than being treated as a final check immediately before release.
3. Understanding Common Application Security Vulnerabilities
Before using AI prompts for security testing, QA professionals should understand the types of weaknesses they are trying to identify.
The OWASP Top 10 provides a widely recognised awareness document describing major web application security risk categories. Its 2025 edition is the latest released version. It can help teams identify important areas for security review, although it is not a complete testing checklist.
Broken Access Control
Broken access control occurs when an application does not properly enforce what authenticated users are allowed to view or do. For example, a customer might be able to access another customer's information because the application does not verify ownership of the requested record.
Security Misconfiguration
Security misconfiguration can occur when applications, servers, cloud services or other components use insecure settings. Examples include unnecessary services, exposed administrative interfaces or inappropriate security headers.
Injection Vulnerabilities
Injection weaknesses can occur when untrusted input is handled unsafely by an application or passed into an interpreter without appropriate protection. SQL injection is one example. Security testing should verify that input is handled safely and that database operations use appropriate protections.
Authentication Failures
Authentication weaknesses may involve inadequate password controls, insecure session handling, weak account recovery processes or missing protections around authentication attempts.
Cryptographic Failures
These weaknesses involve inadequate protection of sensitive data. Testing may examine whether sensitive information is transmitted securely and whether cryptographic controls are implemented appropriately.
Vulnerable and Outdated Components
Applications often depend on external libraries, packages and frameworks. Outdated or vulnerable dependencies can introduce security risks even when the application's own code appears correct.
AI can help testers map application features to relevant security risk categories and suggest areas that deserve further examination. It should not be treated as a substitute for an established security assessment.
4. How Smart AI Prompts Improve Security Testing
The quality of an AI-generated security assessment depends heavily on the information and instructions supplied to the model.
A general prompt such as the following is unlikely to produce a useful assessment:
Find security vulnerabilities in my application.The instruction does not explain the application's purpose, architecture, security requirements, testing scope or expected output.
A more effective prompt provides context and clearly defines the task.
Example: A Structured Security Testing Prompt
You are an experienced application security QA analyst.
I am reviewing a customer-facing web application
in an authorised test environment.
Application functionality:
- User registration
- Customer login
- Password reset
- Customer profile management
- Order history
Testing objectives:
1. Identify potential authentication weaknesses.
2. Review access control requirements.
3. Examine input validation.
4. Identify potential sensitive data exposure.
5. Review session management considerations.
6. Identify relevant security test scenarios.
Instructions:
- Organise the findings by security category.
- Explain why each area requires testing.
- Suggest safe and non-destructive validation checks.
- Identify the evidence required to confirm a finding.
- Do not assume a vulnerability exists without evidence.
- Do not invent application behaviour.
- List missing requirements and clarification questions.
Return a structured security testing checklist.This prompt gives the AI a defined role, application context, testing objectives and expected output. It also makes an important distinction between a possible security weakness and a confirmed vulnerability.
5. AI Prompts for Threat Modelling
Threat modelling is the process of identifying what needs protection, understanding potential threats and determining which security controls should be considered.
It can be performed during application design, before the application is fully developed. AI can help organise information about the application's components, data flows, user roles and external dependencies.
For example, consider an online banking-style application with the following components:
- Customer web application
- Authentication service
- Account management API
- Customer database
- Third-party notification service
Practical AI Prompt: Threat Modelling
Act as an application security analyst.
Help me prepare a preliminary threat model
for the following application.
Application:
A customer portal that allows users to log in,
view account information, update profile details
and access transaction history.
Components:
- Web frontend
- Authentication service
- Backend API
- Customer database
- Third-party notification service
Please identify:
1. Important assets and sensitive information.
2. User roles and trust boundaries.
3. External system dependencies.
4. Potential security threats.
5. Relevant security controls to review.
6. Recommended security test scenarios.
Use a structured threat modelling approach.
Separate confirmed architectural details from assumptions.
Do not claim that any vulnerability has been confirmed.The AI may identify areas such as account takeover risks, unauthorised access to customer records, insecure API communication and excessive exposure of personal information.
These suggestions can help a tester prepare for a more detailed assessment. The threat model must still be reviewed by people familiar with the actual architecture and business requirements.
6. AI-Assisted Secure Code Review
Secure code review involves examining source code to identify implementation patterns that may introduce security weaknesses.
AI can help explain unfamiliar code, identify suspicious patterns and suggest questions for a manual review. This can be particularly useful for QA professionals who are learning how application security relates to programming and software development.
For example, a tester reviewing an application might want to understand whether user input is being validated and whether database operations are using safe parameterisation.
Example AI Prompt: Secure Code Review
Act as a secure code review assistant.
Review the following code from an authorised
application security assessment.
Review objectives:
- Input validation
- Output encoding
- Authentication and authorisation checks
- Sensitive information handling
- Error handling
- Secure database access
- Potential insecure coding patterns
For every potential issue:
1. Identify the relevant code section.
2. Explain the possible security concern.
3. Describe the conditions under which it could matter.
4. Recommend a secure coding approach.
5. Suggest a non-destructive validation test.
6. Identify any additional context needed.
Do not assume that a potential issue is exploitable.
Do not invent missing application logic.
Code:
[Insert approved, sanitised code snippet]AI may help identify areas such as missing validation, unsafe query construction, excessive error details or insufficient permission checks.
However, source code review requires context. A code fragment may appear unsafe in isolation but be protected by validation or controls elsewhere in the application. Findings should be verified against the complete implementation.
7. AI Prompts for API Security Testing
APIs are an important part of modern applications. Websites, mobile applications, microservices and third-party platforms often communicate through APIs.
Because APIs expose application functionality and data, they require careful security testing.
QA professionals should pay particular attention to:
- Authentication requirements for API endpoints.
- Authorisation checks for different user roles.
- Access to records belonging to other users.
- Validation of request parameters and payloads.
- Exposure of sensitive information in responses.
- Rate limiting and resource consumption controls.
- Appropriate error handling and status codes.
Practical AI Prompt: API Security Test Scenarios
Act as an API security QA specialist.
I am reviewing an API in an authorised test environment.
API functionality:
- Customer profile retrieval
- Customer profile update
- Order history retrieval
Available user roles:
- Standard customer
- Customer support representative
- Administrator
Generate a security test scenario checklist covering:
1. Authentication requirements.
2. Role-based authorisation.
3. Access to resources owned by other users.
4. Request parameter validation.
5. Sensitive data exposure.
6. Error response handling.
7. Rate limiting requirements.
For each scenario, provide:
- Test objective
- Preconditions
- Test steps
- Expected result
- Evidence to collect
Use placeholders for endpoint names and test data.
Do not generate destructive requests or tests against
systems outside the authorised scope.This prompt can help create a structured starting point for API security testing. The tester should compare the generated scenarios against the API specification and the application's access-control requirements.
8. Using AI to Analyse Security Findings
Identifying a possible vulnerability is only one part of security testing. QA and security teams must also understand its potential impact, determine whether it is reproducible and communicate the finding clearly.
AI can help organise security observations and prepare draft reports. However, it should not independently assign final severity or declare a vulnerability confirmed without supporting evidence.
Example: AI Prompt for Security Finding Analysis
Act as a security testing report analyst.
Review the following authorised test observation.
Observation:
A customer account request returned profile information
when a different test account's record identifier
was supplied.
Evidence available:
- Request and response captured in the test environment.
- Both test accounts are controlled by the testing team.
- The application is expected to restrict customers
to their own profile information.
Please prepare:
1. A clear description of the observation.
2. The expected security behaviour.
3. The actual observed behaviour.
4. Potential business impact.
5. Additional evidence required.
6. Recommended remediation areas.
7. Questions for the development team.
Do not exaggerate the impact.
Do not assign a confirmed severity without
sufficient supporting information.The AI can help turn a technical observation into a clearer report for developers and stakeholders. The final finding should include reproducible evidence, affected components, business impact and remediation guidance.
9. Security Testing Tools That Can Work Alongside AI
AI prompts become more useful when combined with established application security testing tools. Each tool has a different purpose, and teams should choose tools based on their application architecture, testing requirements and authorisation.
OWASP ZAP
OWASP ZAP is an open-source web application security testing tool. It supports automated scanning, passive analysis and manual testing workflows. AI can help testers understand findings, prepare checklists and organise remediation questions. Scans should be limited to approved environments and configured with appropriate safety controls.
Burp Suite
Burp Suite is a widely used platform for web application security testing. It supports request inspection, application mapping and security assessment workflows. AI can assist with interpreting observations and preparing test documentation, but testers must independently validate findings.
Postman
Postman is commonly used for API development and testing. QA professionals can use it to organise API requests, validate responses and examine authentication behaviour. AI prompts can help generate API test ideas and review expected security controls.
SonarQube
SonarQube supports code quality and code security analysis. It can help identify issues in source code as part of a development workflow. AI can assist in explaining findings and helping developers understand possible remediation approaches.
GitHub Code Security
GitHub security features support security workflows such as code scanning and dependency-related analysis, depending on the features and plan being used. AI can help explain alerts and prepare review questions, while developers and security professionals remain responsible for confirming and addressing issues.
AI does not replace these tools. It can provide an additional layer of assistance in understanding requirements, interpreting results and organising security testing activities.
10. Testing AI-Powered Applications: A Different Security Challenge
There is an important distinction between using AI to perform security testing and testing the security of an application that itself uses AI.
For example, a chatbot that answers customer questions, an AI-powered document assistant or an application that uses a large language model to process business information introduces additional security considerations.
These systems may need testing for:
- Prompt injection: Attempts to manipulate an AI system through instructions contained in user input or external content.
- Sensitive information disclosure: Situations where the AI system reveals information that should remain protected.
- Insecure output handling: Risks created when AI-generated content is used by another application component without appropriate validation.
- Excessive agency: Situations where an AI system has more permissions or can perform more actions than necessary.
- Untrusted external content: Risks from documents, websites or other data sources processed by an AI application.
- Inadequate access controls: Cases where an AI system retrieves or displays information without correctly enforcing user permissions.
For AI-powered applications, security testing should consider both traditional application risks and risks specific to AI systems. OWASP maintains separate resources for large language model application security and AI testing.
11. Best Practices for Using Smart Prompts in Security Testing
AI can produce useful security testing suggestions, but poor prompts can lead to incomplete analysis, false positives or misleading conclusions.
QA professionals should follow these practices:
Provide clear application context
Describe the application, its components, user roles, expected behaviour and testing objectives. Avoid asking AI to assess an application without giving it enough information.
Define the testing scope
Specify which application, API, source code or test environment is in scope. Security testing should only be performed with appropriate permission and within agreed rules of engagement.
Ask AI to distinguish facts from assumptions
Instruct the AI to identify what is known, what is inferred and what needs further investigation. This reduces the risk of treating an unverified suggestion as a confirmed vulnerability.
Use recognised security frameworks
Use resources such as the OWASP Web Security Testing Guide, OWASP Top 10 and Application Security Verification Standard to structure testing activities. AI can help map application features to relevant security areas.
Validate every finding
Review the actual request, response, code, configuration and logs before reporting a security issue. AI-generated findings should never be accepted without supporting evidence.
Protect confidential information
Do not paste production credentials, access tokens, private keys, customer records, confidential source code or sensitive business information into an AI tool unless the organisation has explicitly approved that use and the tool's data-handling arrangements are suitable.
Keep humans responsible for security decisions
AI can suggest test cases and remediation ideas, but final decisions about risk, severity, exploitability and release readiness should be made through the organisation's established security and engineering processes.
12. Common Mistakes When Using AI for Security Testing
- Assuming every AI suggestion is a vulnerability: A potential issue must be investigated and supported by evidence.
- Using vague prompts: Broad instructions often produce generic security advice rather than application-specific test scenarios.
- Ignoring business logic: Automated analysis may not understand all application rules, permissions and workflow requirements.
- Skipping manual validation: AI output must be reviewed against the real application and its architecture.
- Testing systems without permission: Security assessments must follow approved scope, authorisation and testing rules.
- Exposing confidential information: Sensitive data should not be submitted to unapproved AI services.
- Confusing AI testing with AI security: Using AI to help test an application is different from testing the security of an AI-powered application.
- Relying on one tool: Effective security testing combines automated tools, manual review, requirements analysis and technical expertise.
13. Skills QA Professionals Need for AI-Assisted Security Testing
Security testing is a valuable area of knowledge for QA professionals who want to expand beyond traditional functional testing. AI tools can make some activities more accessible, but testers still need a strong understanding of software behaviour and security fundamentals.
Important skills include:
- Software testing fundamentals: Understanding test design, test cases, defect reporting and test execution.
- Web application fundamentals: Understanding browsers, HTTP requests, responses, cookies, sessions and client-server communication.
- API testing: Understanding endpoints, methods, status codes, authentication and request-response structures.
- Security fundamentals: Learning authentication, authorisation, input validation, encryption and common application vulnerabilities.
- OWASP awareness: Understanding the OWASP Top 10, Web Security Testing Guide and Application Security Verification Standard.
- Automation knowledge: Understanding how security checks can be incorporated into testing and development workflows.
- AI prompt engineering: Writing prompts that provide context, constraints, expected outputs and validation requirements.
- Analytical thinking: Evaluating findings, identifying false positives and distinguishing confirmed issues from assumptions.
- Communication: Preparing clear security findings and explaining technical risks to developers and stakeholders.
These skills help QA professionals understand how security fits into the broader software quality process.
14. Learning AI-Assisted Security Testing in Toronto and the GTA
As organisations continue to develop web applications, cloud platforms, APIs and AI-powered services, application security is becoming an important consideration across software development and quality assurance.
QA professionals in Toronto and across the Greater Toronto Area can benefit from learning how functional testing, API testing, automation and security testing work together.
For new graduates and individuals entering the IT industry, understanding security fundamentals can provide a useful foundation for exploring QA and software testing careers. Experienced manual testers can also expand their technical knowledge by learning API security checks, secure test design and AI-assisted analysis.
At STEP IN 2 IT, our focus is on practical IT training that helps learners build relevant technical knowledge and develop skills for modern software testing workflows. Our training approach supports hands-on learning, technical development and career preparation, with job placement assistance where applicable.
Training and job placement assistance do not guarantee employment. Career outcomes depend on individual skills, experience, employer requirements and market conditions.
Explore STEP IN 2 IT's software testing and QA training options to strengthen your understanding of modern testing practices and take the next step in your IT career development.
Frequently Asked Questions
What is AI security testing?
AI security testing uses artificial intelligence to assist with activities such as security test design, threat modelling, source code review, API security analysis and vulnerability report preparation. The output must be validated by qualified testers.
Can AI identify application vulnerabilities?
AI can help identify potential security weaknesses in supplied code, requirements, test observations and application information. However, AI-generated findings may contain false positives or miss important issues. A finding must be verified using appropriate testing and evidence.
What are smart prompts in security testing?
Smart prompts are structured instructions that provide AI tools with a defined role, application context, testing objective, constraints and expected output. They help generate more relevant security test scenarios and analysis than vague instructions.
Can AI replace penetration testers?
No. AI can assist with security analysis, documentation and test preparation, but penetration testing requires technical judgement, an understanding of application architecture, controlled validation and interpretation of real-world risk.
What is OWASP Top 10?
The OWASP Top 10 is a widely recognised awareness document that describes major web application security risk categories. It helps organisations and development teams understand common application security concerns and prioritise security awareness activities.
What tools are used for application security testing?
Common tools include OWASP ZAP, Burp Suite, Postman and SonarQube. Each supports different security testing activities, such as web application assessment, API testing or source code analysis.
Is AI security testing the same as testing AI applications?
No. AI security testing uses AI to assist with security assessments. Testing AI applications examines the security of systems that use AI, including risks such as prompt injection, sensitive information disclosure and insecure output handling.
Can a manual QA tester learn security testing?
Yes. A manual QA tester can build security testing knowledge by learning web application fundamentals, API testing, authentication, authorisation, common vulnerabilities and structured security test design. Automation and AI skills can provide additional capabilities.
Conclusion
Application security is an essential part of software quality. As applications become more connected and complex, QA professionals need to understand not only whether software works but also whether it protects users, data and business processes appropriately.
AI-assisted security testing offers new ways to prepare test scenarios, review code, analyse APIs, organise threat models and document findings. Smart prompts can make these activities more structured and efficient, particularly when testers provide clear context and specific instructions.
However, AI is an assistant, not a security authority. Its suggestions must be checked, vulnerabilities must be validated and all security testing must remain within an authorised scope.
For QA professionals looking to expand their technical skills, combining software testing fundamentals, API knowledge, security awareness and AI prompt engineering is a practical way to prepare for evolving quality assurance workflows.