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Tony Moukbel
Tony Moukbel
Multi-Talented Innovator | 13 Innovations & 4 Patents | Cyber Security Expert | IT & Ai Engineering | 58 Certifications in Cybersecurity, Forensics, Programming & Electronics Dev.
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Secure Debug
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🚨 AI agents need security controls.
Not just better prompts.
As agents start using tools, APIs, memory, data, and automation, the attack surface becomes much bigger.
A secure AI agent architecture should include:
🔹 Agent identity & access
🔹 Just-in-time tool access
🔹 Prompt injection defense
🔹 Output & data protection
🔹 Risk-based action control
🔹 Human-in-the-loop approval
🔹 Sandboxed execution
🔹 Secure memory management
🔹 Cross-agent isolation
🔹 Behavioral monitoring
🔹 Continuous red teaming
🔹 Supply chain security
💡 My biggest takeaway:
AI agent security is not one layer.
It is a stack.
You need to control:
• who the agent is
• what tools it can call
• what data it can access
• what actions it can perform
• how outputs are validated
• how behavior is monitored
• when humans must approve
🚨 The biggest risk is not only that an agent gives a wrong answer.
The bigger risk is that it takes the wrong action with real permissions.
That is why agent security must be built around least privilege, isolation, monitoring, and human oversight.
AI agents can automate work.
But without controls, they can also automate risk.
💬 Which control do you think matters most for AI agents?
Prompt injection defense, tool access, sandboxing, monitoring, or human approval?
AISecurity AgenticAI AIGovernance LLMSecurity CyberSecurity AIAgents RiskManagement ZeroTrust DataSecurity PromptInjection DevSecOps InfoSec
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View Vikramaditya Karanwal’s profile
Vikramaditya Karanwal Verified Profile 2ndVikramaditya Karanwal • 2nd
🛡️AI Security | Securing LLMs, RAG & Agentic AI Systems | DevSecOps | Product, Application, Container & Infrastructure Security | Governance, Risk, Compliance & Audit | Shift-Left Security | Learning Spanish & German
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