Sample Workflows#
This section outlines practical workflows that demonstrate how to use Avenlis Copilot to learn AI security concepts, understand adversarial techniques, and explore mitigation strategies. Each workflow is designed to guide users through specific AI security topics, ensuring effective learning that can be applied in real-world enterprise security strategies, Red Teaming exercises, and Blue Team defense implementations.
Important
AI security is an ongoing process that requires both offensive and defensive strategies:
🔴 Red Teams identify and understand vulnerabilities to enhance security assessments.
🔵 Blue Teams develop mitigation strategies and proactive defenses to strengthen AI models.
Users interact with Avenlis Copilot to gain knowledge and insights, which they can apply within their own security teams and AI security workflows.
🔴 Learning AI Red Teaming Vulnerabilities#
🎯 Goal: Use Avenlis Copilot to understand, identify, and assess vulnerabilities specific to AI and LLM systems.
Start a Query: Initiate a session with Avenlis Copilot to explore common AI security risks.
Example query:
❓ "What are the typical vulnerabilities in Large Language Models?"
Explore Specific Vulnerabilities: Learn about security issues that adversaries may exploit, such as:
Insecure Output Handling: AI models generating harmful or manipulated responses.
Sensitive Information Disclosure: LLMs unintentionally revealing proprietary or confidential data.
Model Theft & Extraction Attacks: Techniques used to steal or replicate proprietary AI models.
Prompt Injection Attacks: Methods for manipulating AI responses using adversarial inputs.
Adversarial Manipulations: Strategies to trick AI models into unintended behaviors.
Review Mitigation Strategies: Ask Avenlis Copilot for security recommendations to counteract these vulnerabilities.
Example query:
❓ "How do I mitigate model extraction attacks?"
Apply the Knowledge: Use insights from Avenlis Copilot to design security assessments and prepare Red Teaming exercises within your organization.
Note
Understanding vulnerabilities is the foundation of AI Red Teaming. Use Avenlis Copilot’s insights to prepare for internal security testing, risk analysis, and organizational security improvements.
🔴 Advanced AI Red Teaming Insights#
🎯 Goal: Leverage Avenlis Copilot to explore advanced AI adversarial attack techniques.
Query Avenlis Copilot for Advanced Attack Simulations:
❓ "Simulate a model inversion attack." ❓ "Demonstrate a prompt injection bypass."
Analyze How These Attacks Work:
Learn how adversaries exploit AI vulnerabilities.
Understand real-world attack patterns, such as:
🛑 Model Inversion Attacks: Extracting training data from AI models.
🔓 Prompt Injection Bypasses: Circumventing security measures through input manipulation.
🎭 Adversarial Sample Generation: Crafting malicious inputs to evade AI defenses.
Generate an AI Red Teaming Plan Using MITRE ATLAS: Example query:
❓ "Generate an AI Red Teaming plan using MITRE ATLAS."
Receive structured guidance based on MITRE ATLAS tactics, including:
Reconnaissance & Discovery: Mapping AI vulnerabilities.
Adversarial Exploitation: Implementing security tests based on AI weaknesses.
Mitigation Strategies: Reducing risk through model hardening and monitoring.
Apply the Knowledge:
Use this learning to structure AI Red Teaming assessments.
Plan internal security training and simulation exercises based on adversarial scenarios.
Warning
AI Red Teaming should always be conducted responsibly. Users must ensure compliance with ethical standards and internal policies when applying adversarial techniques.
🔵 Learning AI Blue Teaming Defenses#
🎯 Goal: Use Avenlis Copilot to learn defensive strategies and strengthen AI security.
Threat Modeling & Risk Assessment:
❓ "What threat models should I use for AI security?"
Learn about various AI threat models and their applicability to enterprise security.
Defensive Security Strategies: Query Avenlis Copilot for guidance on best practices:
❓ "How can I improve my AI system’s resistance to adversarial attacks?"
Learn about key defense mechanisms, including:
Robust Input Validation: Preventing malicious prompt injections.
Model Hardening Techniques: Reducing AI model vulnerabilities.
Adversarial Training: Strengthening AI models against manipulative inputs.
Access Control & Monitoring: Securing APIs and tracking AI interactions.
Incident Response Preparation: - Query Avenlis Copilot for AI security incident response guidelines:
❓ "How do I detect and respond to adversarial AI attacks?"
Learn how to monitor and mitigate security threats in real-time.
Apply the Knowledge:
Implement defensive security measures based on learned strategies.
Use insights from Avenlis Copilot to develop organizational security policies and compliance frameworks.
Tip
AI security requires continuous monitoring, response planning, and adaptation to evolving threats.
📚 Gathering Framework Insights#
🎯 Goal: Use Avenlis Copilot to understand AI security frameworks and integrate them into internal security policies.
Query Framework-Specific Knowledge:
❓ "What is MITRE ATLAS?" ❓ "Explain OWASP Top 10 for LLM."
Learn About Key Security Frameworks:
MITRE ATLAS: Understanding AI-specific adversarial tactics.
OWASP Top 10 for LLMs: Learning about critical AI security vulnerabilities.
Request Breakdown of Security Elements:
❓ "How does OWASP Top 10 help secure AI models?"
Gain structured insights into:
AI risk categories.
Compliance alignment strategies.
Framework-based security controls.
Apply the Knowledge:
Use insights to align internal AI security practices with recognized frameworks.
Leverage security standards to improve governance and compliance.
Tip
Applying framework insights helps ensure security, compliance, and risk reduction in AI systems.
Warning
Avenlis Copilot provides AI security insights, but users must validate findings against official framework documentation and ensure alignment with enterprise security policies.
🚀 Final Thoughts#
Users interact with Avenlis Copilot to learn AI security best practices, adversarial tactics, and defensive strategies. These insights help teams structure AI security assessments, improve governance, and mitigate AI-driven risks.
By following these structured workflows, users can apply security knowledge to enhance AI Red and Blue Teaming strategies within their own organizations. 🔐