Two former RAC employees who sold personal data belonging to car crash victims to claims management companies have been ordered to repay £118,000 under the Proceeds of Crime Act, following earlier sentences of imprisonment and community service. The pair exploited their privileged access to customer data for financial gain, representing a textbook insider threat and data protection failure. The case underscores the real-world financial and legal consequences of misusing access to sensitive personal data.

Architect’s Take: Review and tighten data access controls for employees handling sensitive personal information — implement least-privilege access, robust audit logging, and anomaly detection to identify unusual data exports or queries, particularly in systems holding customer PII.

Original advisory: Duo who sold car crash victims’ data must repay £118k

AWS IoT Device Management has enhanced its connectivity status API to include detailed MQTT session data, such as session timeout and expiry values, plus optional socket-level details including IP addresses, ports, and VPC endpoint IDs. Unlike the IoT Core GetConnection API, which only retains data for 30 minutes post-disconnect, this API stores connection history indefinitely. This is useful for security auditing, forensic investigation of disconnect events, and monitoring connection patterns across large IoT fleets.

Architect’s Take: Review and tighten IAM policies controlling access to the new socket-level details (source/destination IPs, ports, VPC endpoint IDs), as this data could aid lateral movement reconnaissance if exposed to over-privileged roles. Use the indefinite data retention capability to feed IoT connectivity logs into your SIEM for anomaly detection and post-incident forensics.

Original advisory: AWS IoT Device Management adds MQTT session data to connectivity status API

AWS IoT Device Management has enhanced its connectivity status API to include detailed MQTT session data, such as session timeout and expiry values, plus optional socket-level details including IP addresses, ports, and VPC endpoint IDs. Unlike the AWS IoT Core GetConnection API, which only retains data for 30 minutes post-disconnect, this API stores connection history indefinitely, improving long-term auditability. Access to sensitive socket-level information is controlled via IAM policies, allowing organisations to limit visibility to authorised teams.

Architect’s Take: Review and tighten IAM policies governing access to the connectivity status API, particularly the socket-level data permissions, to ensure only operations and security teams have visibility into source/destination IPs and VPC endpoint IDs. Additionally, consider integrating the indefinite data retention capability into your IoT incident response and audit workflows to leverage historical disconnect data for forensic investigations.

Original advisory: AWS IoT Device Management adds MQTT session data to connectivity status API

AWS Step Functions now integrates with Amazon Bedrock AgentCore (currently in preview) to allow AI agent reasoning steps — such as document classification and data extraction — to be embedded directly into automated workflows. This enables multiple agents to run in parallel or sequence within a single workflow, with human approval gates and full audit trails via CloudWatch. For security teams, this introduces AI-driven decision-making into business-critical automation pipelines, expanding the attack surface and governance considerations.

Architect’s Take: Review IAM permissions granted to Step Functions execution roles that invoke AgentCore harnesses, ensuring least-privilege access and that per-invocation model/prompt overrides cannot be manipulated by untrusted inputs. Establish logging and alerting on CloudWatch agent turn details from day one, and apply human approval steps before any agent action with write or destructive permissions.

Original advisory: AWS Step Functions adds AgentCore-powered agentic reasoning step

AWS Step Functions now integrates with Amazon Bedrock AgentCore (currently in preview) to allow AI agent reasoning steps within automated workflows. This enables teams to embed LLM-based tasks such as document classification and data extraction directly into orchestrated pipelines, with parallel execution and human approval gates. Audit trails are available via CloudWatch, capturing agent inputs, outputs, and token usage.

Architect’s Take: Review IAM permissions granted to Step Functions execution roles that invoke AgentCore harnesses — ensure least-privilege policies are applied, particularly around model invocation and tool access. Treat human approval steps as a mandatory control for any agentic action with write or destructive scope, and validate that CloudWatch audit logging is enabled before promoting any AgentCore-integrated workflow to production.

Original advisory: AWS Step Functions adds AgentCore-powered agentic reasoning step