AWS re:Invent 2025 - AI in the Cloud: Surveillance, Sabotage, and Security (AIM242)

Watch	Topic	Details
YouTube	AI Security Strategy	AI is becoming a new battleground for attackers. Modern security requires embracing innovation while staying ahead of adversaries. The attack surface includes data, models, AI pipelines, and runtime.
YouTube	User Security	Users can be both internal employees and external customers. Input and output validation is critical to prevent indirect attacks. Monitoring user interactions with AI applications is essential.
YouTube	Data Poisoning	Data poisoning can manipulate AI model responses. Ensuring data safety in datasets and databases is crucial. Sensitive data used in training must be protected.
YouTube	Model Theft and Manipulation	Models can be stolen and redeployed by attackers. Attackers aim to resell stolen models in the black market. Model theft is a significant threat to intellectual property.
YouTube	AI Pipelines and Shadow AI	AI pipelines can be poisoned or have backdoors injected. Supply chain attacks through open source libraries are common. Shadow AI, where organizations lack visibility into all AI tools used, is a growing problem.
YouTube	Runtime Protection and Guardrails	Relying solely on model safety is insufficient. Runtime guardrails are necessary to protect AI applications. Continuous testing and validation of models are required due to their dynamic nature.
YouTube	AI Scanner and LLM Judge	New AI scanner tool launched to detect sensitive data disclosure, prompt injections, and other risks. LLM Judge embedded in guardrails for real-time protection against OWASP Top 10 risks for LLMs. This represents a new form of virtual patching for AI applications.
YouTube	Infrastructure Security	AI infrastructure is vulnerable to resource exhaustion, GPU hijacking, and data leakage. Weak infrastructure controls can lead to attackers draining resources and increasing costs. Security must be integrated from the start of AI infrastructure development.
YouTube	Network Security	As AI applications become more external, network security is critical. Trend Micro's Zero Day Initiative (ZDI) helps identify and protect against vulnerabilities. API interception, lateral movements, and data exfiltration are key network attack vectors.
YouTube	User Security and Human Factor	Human error remains a significant security risk. Developers and users must be aware of risks associated with external access and AI-driven scams. Monitoring local AI applications and protecting against deep fakes and phishing attacks is essential.
YouTube	Comprehensive AI Security Approach	Securing AI requires a unified security approach across data, models, pipelines, and runtime. Disparate security technologies fail to correlate findings and provide effective mitigation. Trend Micro's AI Security Blueprint offers a comprehensive layer-based protection strategy.
YouTube	Proactive AI Security	Traditional defenses are reactive; a proactive approach is needed. Automated defense mechanisms must evolve as fast as adversaries. Continuous monitoring and adaptive security strategies are crucial for resilience.

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Description

Your AI-powered cloud is humming with activity, running advanced AI applications, but so are your adversaries. Last year we introduced the security for AI blueprint; now it is time to step into the role of operative. Hidden in the layers of data and code are covert risks, adversarial models, poisoned pipelines, and stealthy attacks. In this session, we will decode the enemy’s playbook and arm you with actionable intelligence to stay ahead. Learn how to expose blind spots, neutralize threats, and carry out your mission of keeping your AI-driven cloud secure, no matter how advanced the opposition becomes. This presentation is brought to you by Trend Micro, an AWS Partner.

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