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.

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

Offensive Security

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.

"What are the typical vulnerabilities in Large Language Models?"

Explore Specific Vulnerabilities

Learn about security issues that adversaries may exploit:

Insecure Output Handling: AI models generating harmful or manipulated responses
Sensitive Information Disclosure: LLMs unintentionally revealing proprietary data
Model Theft & Extraction Attacks: Techniques 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.

"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.

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

Advanced Techniques

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 and understand real-world attack patterns:

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

"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

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

Defensive Security

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

"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

"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 to develop organizational security policies and compliance frameworks

AI security requires continuous monitoring, response planning, and adaptation to evolving threats.

Gathering Framework Insights

Knowledge Base

Goal

Use Avenlis Copilot to understand AI security frameworks and integrate them into internal security policies.

Query Framework-Specific Knowledge

"Tell me more about the MITRE ATLAS Framework."

"Explain OWASP Top 10 for LLMs 2025."

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

Applying framework insights helps ensure security, compliance, and risk reduction in AI systems.

Avenlis Copilot provides AI security insights, but users must validate findings against official framework documentation and ensure alignment with enterprise security policies.

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