A new TechBrief from the Association for Computing Machinery's Technology Policy Council examines the rapid rise of agentic AI systems capable of planning and executing multi-step tasks autonomously. As adoption scales among enterprises and consumers, existing legal, regulatory, and technical frameworks are struggling to keep pace with these autonomous capabilities.
The report highlights several critical policy dimensions:
- Ambiguity in legal liability when an autonomous system causes harm without a clear accountable party.
- Serious security risks arising from the inability of models to distinguish between data and malicious commands.
- A lack of consumer transparency regarding agent permissions, authority, and recourse.
- Workforce disruption concerns where productivity claims have not been independently verified at scale.
The report highlights several critical policy dimensions:
- Ambiguity in legal liability when an autonomous system causes harm without a clear accountable party.
- Serious security risks arising from the inability of models to distinguish between data and malicious commands.
- A lack of consumer transparency regarding agent permissions, authority, and recourse.
- Workforce disruption concerns where productivity claims have not been independently verified at scale.
Three vendors – Cohesity, ServiceNow, and Datadog – have partnered to create a recoverability service designed to address the risks associated with agentic AI (AIOps). The service aims to restore systems to a "trusted state" by identifying and recovering files and data corrupted by AI errors or malicious attacks.
The companies anticipate increased adoption of agentic AI for system operation but recognize the potential for errors and vulnerabilities. Their solution focuses on preserving immutable snapshots of AI environments, enabling point-in-time recovery of agents, data, and infrastructure components, including vector stores and agent memory.
ServiceNow and Datadog provide control and observability platforms to detect anomalies, triggering API-driven restorations when problems are identified. This offering competes with Rubrik's similar tool and native rollback capabilities from vendors like Cisco. Gartner predicts a significant increase in the integration of task-specific agents in enterprise applications, while Forrester emphasizes the need for guardrails and strong oversight in agentic AI development.
The companies anticipate increased adoption of agentic AI for system operation but recognize the potential for errors and vulnerabilities. Their solution focuses on preserving immutable snapshots of AI environments, enabling point-in-time recovery of agents, data, and infrastructure components, including vector stores and agent memory.
ServiceNow and Datadog provide control and observability platforms to detect anomalies, triggering API-driven restorations when problems are identified. This offering competes with Rubrik's similar tool and native rollback capabilities from vendors like Cisco. Gartner predicts a significant increase in the integration of task-specific agents in enterprise applications, while Forrester emphasizes the need for guardrails and strong oversight in agentic AI development.
NIST is launching a new project around standards for artificial intelligence agents, seeking feedback on the secure use of the rapidly evolving technology. The initiative focuses on security concerns arising from the autonomous nature of AI agents and aims to foster interoperability and public trust. It includes a request for information on AI agent security and a draft concept paper on software and AI agent identity and authorization.
