This article outlines various security risks associated with AI agents and their infrastructure, including issues like chat history exfiltration and prompt injection. It emphasizes the need for a comprehensive security platform to monitor and govern AI operations effectively.

The article discusses the importance of treating AI agent memory as a critical database, emphasizing the need for security measures like firewalls and access controls. It highlights the risks of memory poisoning, tool misuse, and privilege creep, urging organizations to integrate memory management with established data governance practices.

The article examines the security risks associated with the Model Context Protocol (MCP), which enables dynamic interactions between AI systems and external applications. It highlights vulnerabilities such as content injection, supply-chain attacks, and the potential for agents to unintentionally cause harm. The authors propose practical controls and outline gaps in current AI governance frameworks.

RBC developed a comprehensive Infrastructure as Code strategy to manage its hybrid cloud environment effectively. The approach emphasizes security, cost control, and streamlined developer workflows, enabling consistent deployments across public and private infrastructures. Future plans include leveraging AI for enhanced automation and operational efficiency.

This article discusses how the promise of DevOps often overlooks governance, leading to a lack of accountability in automated deployments. It highlights the emerging role of GRC Engineers, who integrate governance, risk, and compliance directly into DevOps practices, ensuring that security and compliance are built into the development process.

A survey reveals over half of AI agents used by companies in the US and UK lack proper monitoring and security. Experts warn that this gap poses significant risks, with many organizations unaware of the number and capabilities of their deployed agents. The unchecked growth of AI agents could lead to serious security incidents.

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Open source security governance remains a significant challenge for organizations, as they struggle to effectively manage vulnerabilities in widely used components. The article emphasizes the importance of understanding the systemic risks associated with these components and advocates for a proactive governance approach that includes standardized dependency management, defined ownership, and continuous capability-building. Ultimately, it highlights that successful governance is an ongoing operational discipline rather than a one-off task.

Lido DAO is initiating a vote to address concerns over a compromised oracle that has affected its platform. This decision reflects ongoing challenges in maintaining security and integrity within decentralized finance systems. The community is being called to participate in the governance process to determine the next steps.

GitOps significantly reduces the need for elevated access by promoting declarative, version-controlled workflows that automate system state management. While high-performing teams recognize the security benefits of GitOps, they also understand that Git itself must be treated as a sensitive access point, necessitating robust governance and security measures. Organizations should aim for continuous improvement in their GitOps practices while ensuring exceptions to these practices are well-documented and controlled.

The article discusses the challenges posed by agentic artificial intelligences (AIs) in the context of the OODA loop—Observe, Orient, Decide, Act—framework. It highlights the complexities of integrating AI decision-making into human processes and the implications for security and governance. The author emphasizes the need for a deeper understanding of these interactions to ensure effective management of AI systems.

The Automated Governance Maturity Model has been introduced to help organizations navigate the complexities of governance in an era dominated by AI-generated code. This model provides a framework for assessing capabilities across policy, evaluation, enforcement, and audit, enabling organizations to automate governance processes effectively. Feedback is encouraged to refine the model and expand its practices and guidance.

Geordie empowers organizations to safely scale Agentic AI by providing security teams with essential visibility, risk intelligence, and control. Their innovative approach combines governance with technological advancement, allowing enterprises to manage risks associated with AI agents effectively. This enables seamless collaboration between security and business teams, fostering confident growth in agentic innovation.

The on-demand webinar discusses strategies for managing SaaS sprawl, highlighting the challenges faced by IT and security teams due to the rapid growth of SaaS applications and AI tools. It emphasizes the importance of proactive SaaS governance to enhance visibility, optimize spending, and automate processes for onboarding and offboarding users.

The article discusses the importance of governance in managing data lakes, emphasizing the need for structured oversight and compliance to ensure data quality and security. It highlights strategies for implementing effective governance frameworks and the role of tools in facilitating better data management practices.

The document outlines security recommendations for organizations using Microsoft Intune to enhance their security posture, following industry standards and Microsoft's own guidelines. It emphasizes the importance of securing tenants, devices, and data, and provides a framework for implementing various security controls based on available licenses.

IBM is addressing the challenge of shadow AI, which refers to the use of unauthorized or unregulated artificial intelligence tools within organizations. The company emphasizes the importance of governance and security measures to mitigate risks associated with these tools, ensuring that enterprises can leverage AI effectively while maintaining control over their data and processes.