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Introduction:
The convergence of Generative AI and Agentic AI is not just another technological evolution—it is a paradigm shift that fundamentally redefines the cybersecurity landscape. While Generative AI empowers creation at scale, Agentic AI takes it a step further by autonomously executing tasks across web, software, and physical environments, creating new and amplified security risks distinct from both traditional AI safety and conventional software security. As organizations rush to deploy autonomous AI agents—with 76% already piloting or rolling them out—the security community faces an urgent question: are we building the future on a foundation of sand?
Learning Objectives:
- Understand the core distinctions between Generative AI, LLMs, and Agentic AI, and their respective security implications
- Identify and mitigate the OWASP Top 10 risks specific to agentic AI systems, including prompt injection, privilege escalation, and tool manipulation
- Implement practical defense strategies, from runtime monitoring to governance frameworks, for securing AI deployments
You Should Know:
1. Understanding the Agentic AI Threat Model
Agentic AI systems differ fundamentally from traditional software and even conventional LLMs. Unlike a chatbot that simply responds to prompts, an agentic AI system can think, plan, and act autonomously—interacting with web browsers, code interpreters, internal databases, email clients, and enterprise APIs. This autonomy, combined with persistence, dramatically expands the attack surface. The security challenges are not merely theoretical: the UK’s National Cyber Security Centre (NCSC) warns that agentic AI systems inherit known LLM risks like susceptibility to jailbreaking and prompt injection, with security challenges evolving as the technology matures.
The threat model encompasses several unique vectors:
- Indirect Prompt Injections: Malicious instructions hidden in data that an agent retrieves and acts upon
- Privilege Escalation: An agent exploiting its own or another agent’s credentials to gain unauthorized access
- Tool and Protocol Risks: Agents interacting with external systems in unintended ways
- Multi-Agent Manipulation: Coordinated attacks exploiting interactions between multiple agents
The Australian Cyber Security Centre emphasizes that information continuously flows between AI and non-AI systems, increasingly blurring defensive boundaries and making it difficult to isolate AI-related risks.
- OWASP Top 10 for Agentic AI: A Practical Breakdown
The OWASP Foundation has published a specific Top 10 list for Agentic AI systems, focusing on how threat actors can exploit these systems in new ways. The most critical risks include:
- Identity and Privilege Abuse: AI agents exploiting credentials, roles, or trust relationships to gain additional access
- Insecure Agent Communication: Interception or manipulation of data exchanged between agents
- Insecure Plugin/Tool Design: Vulnerabilities in the tools and plugins agents use to interact with external systems
- Excessive Agency: Granting agents more permissions or capabilities than necessary
- Agent Sprawl: Losing track of deployed agents and their configurations
Mitigating these risks requires a fundamental shift in defense strategy—from traditional perimeter-based security to a focus on runtime reasoning governance and overseeing not just access but the decisions an agent can make without a human in the loop.
- Securing the AI Lifecycle: From Training to Inference
Security cannot be an afterthought in AI deployments. The Certified GenAI & LLM Cybersecurity Professional program identifies vulnerabilities across the entire AI lifecycle, including model training, fine-tuning, inference pipelines, and API integrations. Key areas to address include:
Data Poisoning: Attackers can corrupt training data to influence model behavior. Implement data provenance verification and anomaly detection.
Model Inversion: Adversaries can extract sensitive training data from models. Use differential privacy and output sanitization.
Prompt Injection: Crafted inputs can override system instructions. Implement input validation, context isolation, and output filtering.
Supply Chain Risks: Vulnerabilities in dependencies, pre-trained models, or third-party APIs. Conduct thorough vetting and maintain an SBOM (Software Bill of Materials).
For Linux environments, monitoring AI workloads can be achieved with:
Monitor GPU usage and process activity nvidia-smi --query-gpu=utilization.gpu,memory.used --format=csv -l 5 Check for unusual network connections from AI processes sudo netstat -tunap | grep python Audit file access patterns for training data sudo auditctl -w /path/to/training/data -p rwxa -k ai_data_access
For Windows environments, use PowerShell to monitor AI processes:
Monitor Python/ML processes
Get-Process python, tensorflow, pytorch | Select-Object Name, CPU, WorkingSet
Check for suspicious network connections
Get-1etTCPConnection | Where-Object {$_.State -eq "Established"} | Select-Object LocalAddress, LocalPort, RemoteAddress, RemotePort
4. Runtime Monitoring and Policy Enforcement
Given the autonomous nature of agentic AI, runtime monitoring is critical. The SAFE-AGENT framework proposes runtime monitoring and policy enforcement for tool-using LLM agents in high-stakes enterprise workflows. Key capabilities include:
- Tool Usage Auditing: Logging every tool invocation, including parameters and results
- Anomaly Detection: Identifying deviations from expected behavior patterns
- Policy Enforcement: Blocking or flagging actions that violate security policies
- Human-in-the-Loop Escalation: Requiring human approval for high-risk actions
Implementation considerations for monitoring agent behavior:
Linux (using auditd and custom scripts):
Monitor API calls from AI agents sudo strace -p <PID> -e trace=network -o agent_network.log Real-time log monitoring for suspicious patterns tail -f /var/log/agent_activity.log | grep -E "ERROR|WARNING|unauthorized"
Windows (using PowerShell and Event Viewer):
Enable PowerShell script block logging for AI automation scripts
Set-ItemProperty -Path "HKLM:\SOFTWARE\Policies\Microsoft\Windows\PowerShell\ScriptBlockLogging" -1ame "EnableScriptBlockLogging" -Value 1
Monitor for new processes launched by AI agents
Register-WmiEvent -Query "SELECT FROM Win32_ProcessStartTrace" -Action { Write-Host "Process started: $($Event.SourceEventArgs.NewEvent.ProcessName)" }
5. AI Security Training and Workforce Development
The skills gap in AI security is widening. Organizations need professionals who understand both AI technologies and security principles. Several training programs have emerged to address this need:
- Certified Trustworthy GenAI Specialist (CT-GENAI): Addresses data poisoning, model inversion attacks, and malicious content generation
- Security and Privacy in Agentic Systems: A one-day intensive for AI engineers, security architects, and privacy professionals
- Agentic AppSec: Harnessing LLMs for developers and cybersecurity professionals
- AgentCyTE: A modular framework integrating LLM-based reasoning with network emulation to automate cybersecurity training scenario generation
The CyberJustice Tutor represents an innovative approach to cybersecurity education—an Agentic AI framework employing a “Think-Plan-Act” cognitive cycle for autonomous goal decomposition and dynamic context maintenance.
6. Governance and Regulatory Compliance
Government and international partners are increasingly focusing on AI security. CISA, along with US and international partners, has released guidance on the secure adoption of agentic AI, noting that critical infrastructure and defense sectors are increasingly deploying these systems. The guidance highlights risks including expanded attack surface, privilege creep, behavioral misalignment, and obscure event records.
Key governance recommendations include:
- Implement NIST AI Risk Management Framework: Apply structured risk assessment to agentic AI deployments
- Establish Clear Accountability: Define who is responsible for AI actions and outcomes
- Conduct Regular Security Assessments: Include red teaming and penetration testing of AI systems
- Maintain Incident Response Plans: Specifically for AI-related security incidents
- Ensure Audit Trails: Maintain comprehensive logs of AI decisions and actions
What Undercode Say:
- Key Takeaway 1: Agentic AI represents a fundamental shift from reactive to proactive security—but it also introduces unprecedented risks that require new defense paradigms, moving beyond perimeter security to runtime reasoning governance.
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Key Takeaway 2: The security community must act now to develop standards, training, and tools for securing agentic AI. With 52% of organizations not fully confident their security controls would detect a compromised AI agent, the window for proactive defense is closing rapidly.
Analysis: The intersection of Generative AI and Agentic AI is creating a perfect storm for cybersecurity. On one hand, these technologies offer unprecedented capabilities for defense—autonomous threat hunting, continuous monitoring, and adaptive incident response at scale. On the other, they introduce novel attack vectors that traditional security controls cannot address. The risks extend beyond technical vulnerabilities to include governance, accountability, and ethical concerns. Organizations must adopt a holistic approach that combines technical controls, workforce training, and robust governance frameworks. The emergence of OWASP Top 10 for Agentic AI, NCSC guidance, and specialized training programs indicates that the industry is waking up to these challenges—but awareness alone is not enough. Concrete action, investment in security, and a culture of continuous learning are essential.
Prediction:
- +1 The maturation of Agentic AI security frameworks will drive the creation of a new cybersecurity specialty—AI Security Engineering—with high demand and premium compensation, potentially reshaping the cybersecurity job market by 2028.
- +1 Runtime monitoring and policy enforcement tools like SAFE-AGENT will become as ubiquitous as firewalls and antivirus are today, creating a multi-billion dollar market for AI security solutions.
- -1 The rapid adoption of agentic AI without adequate security controls will lead to a major security breach within the next 18-24 months, potentially involving data exfiltration or system manipulation by a compromised AI agent.
- -1 Regulatory fragmentation—with different standards across jurisdictions—will create compliance nightmares for global enterprises, slowing innovation and creating security gaps.
- +1 The CyberJustice Tutor and similar Agentic AI educational frameworks will revolutionize cybersecurity training, making it more adaptive, personalized, and effective at scale.
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