Next Steps

Congratulations on completing Red Teaming AI Systems! You now have the skills to systematically identify vulnerabilities in AI systems through professional adversarial testing.

What You've Learned

Throughout this course, you've mastered:

Module	Key Skills
Introduction	Red team mindset, OWASP methodology
Environment Setup	DeepTeam, Garak, PyRIT tools
Attack Techniques	Multi-turn attacks, jailbreaks, evasion
Vulnerability Assessment	OWASP mapping, RAG/agent testing
Metrics & Reporting	ASR measurement, professional reports
Continuous Testing	CI/CD integration, team building

Apply Your Skills

Start practicing immediately:

1. Set Up Your Testing Environment

# Create your red team workspace
from pathlib import Path
import json

def initialize_red_team_workspace(workspace_name: str) -> Path:
    """
    Set up a professional red team workspace.
    Cross-platform compatible.
    """
    workspace = Path.home() / "red_team" / workspace_name

    directories = [
        "assessments",
        "reports",
        "evidence",
        "tools",
        "templates"
    ]

    for dir_name in directories:
        (workspace / dir_name).mkdir(parents=True, exist_ok=True)

    # Create configuration file
    config = {
        "workspace_name": workspace_name,
        "created_date": "2025-12-20",
        "tools": ["deepteam", "garak", "pyrit"],
        "default_model": "gpt-4"
    }

    config_path = workspace / "config.json"
    with open(config_path, "w") as f:
        json.dump(config, f, indent=2)

    print(f"Workspace created at: {workspace}")
    return workspace


# Initialize your workspace
workspace = initialize_red_team_workspace("my_first_assessment")

2. Run Your First Assessment

# pip install deepteam python-dotenv

from deepteam import RedTeamer, Vulnerability
from pathlib import Path
from dotenv import load_dotenv

# Load API keys from .env file
load_dotenv()

def run_first_assessment():
    """
    Run a beginner-friendly assessment.
    Start with common vulnerabilities.
    """
    red_teamer = RedTeamer(
        model="gpt-4",
        vulnerabilities=[
            Vulnerability.PROMPT_INJECTION,
            Vulnerability.JAILBREAK,
            Vulnerability.PII_LEAKAGE,
        ],
        max_attempts_per_vulnerability=10
    )

    print("Starting vulnerability scan...")
    results = red_teamer.scan()

    print("\nResults Summary:")
    for vuln, data in results.items():
        asr = data.get("success_rate", 0)
        status = "VULNERABLE" if asr > 20 else "PASSING"
        print(f"  {vuln}: {asr}% ASR [{status}]")

    return results


# Run your first assessment
results = run_first_assessment()

3. Document Your Findings

Use the templates and frameworks from this course to create professional reports.

Recommended Learning Path

Based on your new skills, here's where to go next:

┌─────────────────────────────────────────────────┐
│      ✓ Red Teaming AI Systems (COMPLETED)       │
└─────────────────────┬───────────────────────────┘
                      │
                      ▼
┌─────────────────────────────────────────────────┐
│        LLM Guardrails in Production             │
│   Learn to build the defenses you tested!       │
│                                                 │
│   Topics:                                       │
│   • Advanced guardrail architectures            │
│   • Content moderation at scale                 │
│   • Input/output filtering pipelines            │
│   • Compliance frameworks (GDPR, SOC2)          │
│   • Production monitoring and alerting          │
└─────────────────────────────────────────────────┘

Why Learn Guardrails Next?

As a red teamer, you now understand how attacks work. The next step is understanding how to build effective defenses:

Red Team Skill	Guardrails Application
Prompt injection attacks	Design injection-resistant prompts
Multi-turn exploitation	Implement conversation monitoring
Data extraction techniques	Build output filtering layers
Tool abuse vectors	Create permission boundaries

Connection: The best security professionals understand both offense and defense. Your red team skills make you better at building guardrails, and guardrail knowledge makes you a more effective red teamer.

Community & Resources

Continue learning with these resources:

Official Documentation

Research Papers

Multi-turn attack research (Crescendo, Siege)
OWASP LLM Top 10 (2025 edition)
AI safety evaluation frameworks

Practice Platforms

AI-specific CTF challenges
Bug bounty programs (OpenAI, Anthropic, Google)
Open-source LLM testing

Your Red Team Checklist

Before your next assessment, ensure you:

Have proper authorization documented
Set up isolated testing environment
Installed and configured testing tools
Reviewed target system documentation
Prepared report templates
Established communication channels with stakeholders
Defined scope and rules of engagement

Final Thoughts

Red teaming AI systems is both an art and a science. The techniques you've learned will evolve as AI systems become more sophisticated. Stay curious, keep learning, and always test responsibly.

Remember: The goal of red teaming isn't to break things—it's to make them stronger.

Ready to build defenses? Continue your journey with LLM Guardrails in Production to learn how to implement the protections that stop the attacks you've mastered. :::

Quiz