Agentic AI - Risk and Cybersecurity Masterclass 2026

Explore agentic AI design patterns and architectures, including reflection, tool usage, planning and reasoning, and multi-agent collaboration, with single, distributed, and supervised architectures and practical examples.

See how to set up and run AI agents with a simple multi-agent framework, using Crew AI and OpenAI API keys, to research topics and generate reports.

Explore agentic AI use cases in cybersecurity, from autonomous threat analysis and incident response to AI-driven SOCs and virtual CISOs, with adaptive security postures and zero-trust enforcement.

Explore agentic ai in cybersecurity, using a swarm of specialized agents to analyze architectures, perform stride threat modeling, and design a secure authentication system with multi-factor authentication.

Identify how agentic AI differs from generative AI by examining autonomy-driven risks and the threat surface. Learn existing AI vulnerabilities and emerging cybersecurity challenges, with emphasis on applying strong controls.

Explains how bias, an inherent AI risk from biased training data, can skew decision making in loans and policing. Advocates bias testing, human-in-the-loop review, and auditable decision processes.

Understand transparency in ai and how explainable ai mitigates bias amplified by agentic ai, reinforcing accountability, trust, and compliance with eu ai act and nist ai risk management framework.

Explore AI model lifecycles that reuse pre-built models trained on company data, exposed via APIs, and study data tags alongside attacks like data poisoning, model evasion, and extraction.

Autonomy drives both the benefits and risks of agentic AI, risking misinterpretation, misconfiguration, and service disruptions; apply guardrails, human-in-the-loop oversight, sandbox testing, and quick rollback.

Address accountability risks in agentic AI by establishing AI governance policy, maintaining a decision log, and ensuring human oversight and explainable AI to prevent wrongful decisions and liability.

Misalignment in autonomous agentic ai causes goals to diverge from human intent, triggering unintended and harmful actions. Reinforce guardrails, real-time oversight, and failsafes to prevent cascading failures in multi-agent systems.

Disempowerment risks emerge as over reliance on agentic AI erodes human problem solving and creativity. Balance deployment with augmentation, reskilling, and strong human oversight.

Explore how agentic AI amplifies cyber threats through autonomous, end-to-end attacks—phishing, DDoS, and ransomware—driving a 24/7 army of AI attackers and how defense teams can counter with AI-driven safeguards.

Explore AI agent hijacking caused by prompt injections—direct and indirect—and learn how least privilege, monitoring, and red teaming help prevent abuse.

Explore agentic ai pattern vulnerabilities in distributed and supervisor architectures, including rogue agents, impersonation, emergent behavior, and inter-agent communication exploits.

Design and implement an agentic AI security framework through governance, leadership alignment, and a task force. Tailor controls via risk assessment and threat modeling, using NIST, EU AI act, ISO.

Explore threat modeling for agentic AI, focusing on the maestro framework across seven layers—from foundational models to the agent ecosystem—to identify multi-agent risks and guide defenses.

Apply the maestro framework for threat modeling agenda to agentic ai, detailing the foundation model, data operations, and generic frameworks with practical mitigations.

Explore layer five evaluation and observability for Agentic AI, including observability tools, logging, anomaly detection, and compliance monitoring to guard against tampering and data leakage.

Apply the maestro framework to a practical seven-layer threat modeling case study for agentic AI in finance, with layer decomposition, cross-layer risk identification, mitigation, and continuous monitoring.

Explore the rise of OpenClaw and personal agentic assistants, their architecture, and security risks, including prompt injections, memory poisoning, credential leakage, and threat modeling using the Maestro framework.

Model OpenClaw threats with Maestro’s seven-layer framework, tracing prompts, memory, tools, and sub-agents; apply cross-layer mitigations like validation, isolation, and zero-trust.

Explore the agentic AI security scoping matrix, detailing four autonomy scopes from no to full agency, and the six security dimensions for implementing secure agentic architectures.

Explore the model context protocol, a standardized open framework that connects AI models to data sources and tools, enabling secure, scalable agent workflows and highlighting security risks with mitigation.

Explore emerging MCP security risks, including malicious servers, rugpull, tool poisoning, and cross tool poisoning, within the client–server architecture and the principle of least privilege.

Evaluate MCP servers before use by checking downloads, recency, and provider, then use Lama’s security rating to gauge vulnerabilities and git clone followed by a security scan with Q developer.

Discover how agentic AI is here now, moving from forecasting to autonomous task execution, with new roles like AI cybersecurity, AI agent owners, and risk.