Mitigating the Risks of Agentic AI
Unchecked agentic AI can lead to data leaks, financial fraud, and systemic instability if not properly governed. This report provides a framework for building resilient, secure, and compliant autonomous systems.
- Identify the four critical risk lenses: Privacy, Fraud, Safety, and Influence Operations.
- Learn to detect unusual agent behaviors and communication poisoning.
- Implement actionable mitigation strategies, from guardrails to continuous red teaming.
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Overview
As AI transitions from simple chatbots to autonomous agents capable of independent reasoning and execution, the attack surface for enterprise organizations has expanded significantly. Unlike traditional Generative AI, agentic systems move beyond single-turn interactions to orchestrate tools, query external APIs, and coordinate with other agents. While this increases efficiency, it also introduces complex vulnerabilities like prompt injection, tool hijacking, and goal manipulation.
Our latest research, "Mitigating the Risks of Agentic AI," dives deep into the security challenges inherent in these autonomous workflows. We examine how bad actors exploit agentic vulnerabilities to trigger large-scale misinformation campaigns, market instability, and critical infrastructure failures. By exploring real-world failure points—such as credential leakage and rogue agent behavior—this report provides a proactive roadmap for developers and security leaders. Discover how to balance innovation with safety by deploying real-time guardrails and expert red-teaming methodologies to ensure your AI agents remains accountable and secure.
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What’s New from Alice
Securing Agentic AI: The OWASP Approach
In this episode, Mo Sadek is joined by Steve Wilson (Chief AI and Product Officer at Exabeam, founder and co-chair of the OWASP GenAI Security Project) to explore how OWASP is shaping practical guidance for agentic AI security. They dig into prompt injection, guardrails, red teaming, and what responsible adoption can look like inside real organizations.
Distilling LLMs into Efficient Transformers for Real-World AI
This technical webinar explores how we distilled the world knowledge of a large language model into a compact, high-performing transformer—balancing safety, latency, and scale. Learn how we combine LLM-based annotations and weight distillation to power real-world AI safety.
