vff
NewsTrending

AWS Adds Domain Filtering for AI Agents

Kosti VasilakakisRead original
Share
AWS Adds Domain Filtering for AI Agents

Amazon Bedrock AgentCore now supports domain-level access controls via AWS Network Firewall, allowing enterprises to restrict AI agent web browsing to approved domains. The capability addresses security and compliance concerns around unrestricted internet access, data exfiltration, and prompt injection attacks. Organizations can implement allowlists, block specific categories, log connection attempts, and apply default-deny policies to agent traffic when deployed in a VPC.

Amazon Bedrock AgentCore now supports domain-level access controls via AWS Network Firewall, allowing enterprises to restrict AI agent web browsing to approved domains. The capability addresses security and compliance concerns around unrestricted internet access, data exfiltration, and prompt injection attacks. Organizations can implement allowlists, block specific categories, log connection attempts, and apply default-deny policies to agent traffic when deployed in a VPC.

  • AWS Network Firewall can filter AgentCore agent traffic to approved domains using SNI inspection and domain-based rules
  • Enterprises can create allowlists (e.g., wikipedia.org, stackoverflow.com), block categories like social media, and log all connection attempts for audit trails
  • Multi-tenant SaaS providers can implement per-customer network policies with execution-specific blocking and regional restrictions
  • Default-deny policies and managed rules against botnets and malware domains reduce attack surface from prompt injection and unauthorized data access

AI agents with web access create new security and compliance risks for regulated industries. Domain-level filtering is a foundational control that enterprises require before deploying agents in production, addressing both data exfiltration concerns and prompt injection attack vectors. This capability moves agent security from theoretical to operationally feasible for risk-averse organizations.

  • Domain-level filtering becomes table stakes for enterprise AI agent deployments, shifting from optional to required security control
  • Multi-tenant SaaS platforms can now differentiate on security posture by offering granular, per-customer network policies that competitors without this capability cannot match
  • Prompt injection attacks become significantly harder to exploit when agents cannot reach arbitrary domains, reducing the practical threat surface of agent-based applications
  • Compliance and audit requirements around egress control and data exfiltration prevention become easier to satisfy with logging and allowlist enforcement
Share

Our Briefing

Weekly signal. No noise. Built for founders, operators, and AI-curious professionals.

No spam. Unsubscribe any time.

Related stories

AI Discovers Security Flaws Faster Than Humans Can Patch Them

AI Discovers Security Flaws Faster Than Humans Can Patch Them

Recent high-profile breaches at startups like Mercor and Vercel, combined with Anthropic's disclosure that its Mythos AI model identified thousands of previously unknown cybersecurity vulnerabilities, underscore growing demand for AI-powered security solutions. The article argues that cybersecurity vendors CrowdStrike and Palo Alto Networks, which are integrating AI into their threat detection and response capabilities, represent undervalued investment opportunities as enterprises face mounting pressure to defend against both conventional and AI-discovered attack vectors.

21 days ago· The Information
AWS Launches G7e GPU Instances for Cheaper Large Model Inference
TrendingModel Release

AWS Launches G7e GPU Instances for Cheaper Large Model Inference

AWS has launched G7e instances on Amazon SageMaker AI, powered by NVIDIA RTX PRO 6000 Blackwell GPUs with 96 GB of GDDR7 memory per GPU. The instances deliver up to 2.3x inference performance compared to previous-generation G6e instances and support configurations from 1 to 8 GPUs, enabling deployment of large language models up to 300B parameters on the largest 8-GPU node. This represents a significant upgrade in memory bandwidth, networking throughput, and model capacity for generative AI inference workloads.

29 days ago· AWS Machine Learning Blog
Anthropic Launches Claude Design for Non-Designers
Model Release

Anthropic Launches Claude Design for Non-Designers

Anthropic has launched Claude Design, a new product aimed at helping non-designers like founders and product managers create visuals quickly to communicate their ideas. The tool addresses a gap for early-stage teams and individuals who need to share concepts visually but lack design expertise or resources. Claude Design integrates with Anthropic's Claude AI platform, leveraging its capabilities to streamline the visual creation process. The launch reflects growing demand for AI-powered design tools that lower barriers to entry for non-technical users.

about 1 month ago· TechCrunch AI
Google Splits TPUs Into Training and Inference Chips

Google Splits TPUs Into Training and Inference Chips

Google is splitting its eighth-generation tensor processing units into separate chips optimized for AI training and inference, a shift the company says reflects the rise of AI agents and their distinct computational needs. The training chip delivers 2.8 times the performance of its predecessor at the same price, while the inference processor (TPU 8i) achieves 80% better performance and includes triple the SRAM of the prior generation. Both chips will launch later this year as Google continues its effort to compete with Nvidia in custom AI silicon, though the company is not directly benchmarking against Nvidia's offerings.

28 days ago· Direct