Results for “embedded-security”

Enforce Kubernetes Pod Security Standards at the namespace level using the built-in admission controller, with support for baseline and restricted profiles.

Builds AWS Nitro Enclave-based confidential computing environments with cryptographic attestation, KMS policy integration, and secure vsock communication for processing sensitive data.

Deploy MATLAB-generated code to embedded hardware using Embedded Coder. Use when configuring code generation for microcontrollers (STM32, Raspberry Pi, ARM Cortex), setting up PIL/SIL verification, disabling dynamic memory allocation, or configuring hardware-specific code generation settings. Covers ERT-based configurations, processor-in-the-loop testing, memory constraints, and the MEX→SIL→PIL verification progression.

Deploy and operate the VSS 3.2 GA RT-Embed Video Embedding microservice using Docker Compose, covering GPU prerequisites, REST API usage for file uploads, text/video embeddings, live RTSP streams, Redis/Kafka/OTel integration, and troubleshooting.

Enforce Pod Security Standards (Privileged, Baseline, Restricted) in Kubernetes 1.25+ using the Pod Security Admission controller with namespace labels and compliant pod specs.

Integrate Hardware Security Modules (HSMs) using the PKCS#11 interface for cryptographic key management, signing operations, and secure key storage with python-pkcs11, AWS CloudHSM, and YubiHSM2.

Hardens managed Kubernetes clusters on EKS, AKS, and GKE by implementing Pod Security Standards, network policies, workload identity, RBAC scoping, image admission controls, and runtime security monitoring.

Implements Kubernetes network policies for micro-segmentation, covering ingress/egress rules, Calico and Cilium, with a Python CLI for managing and validating policies.

Restricts unauthorized removable media access on endpoints by implementing USB device control policies via Group Policy, Intune, or EDR platforms to prevent data exfiltration and malware introduction.

Assess the security posture of Kubernetes etcd clusters by evaluating encryption at rest, TLS configuration, access controls, backup encryption, and network isolation.

Containerized security auditing and ethical hacking tools. All operations run in isolated Docker containers for safety.

Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or imported models rebuilt as dlnetwork for lean hardware, (2) direct C/C++ code generation from PyTorch and LiteRT models. Both patterns support all targets (Cortex-M/A/R, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, loadLiteRTModel, importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromTensorFlow, importNetworkFromKeras, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.

Audits codebases for leaked secrets and hardcoded credentials, generates .env templates, configures secrets management with AWS Secrets Manager, Vault, Doppler, or GCP Secret Manager, sets up credential rotation, and integrates secrets into CI/CD pipelines via OIDC federation.

Performs a full-stack security posture assessment with 0-100 risk scoring, scanning dependencies, code patterns, configuration, and data handling, then produces a prioritized report and routes to specialized skills.

Deliberately disables or degrades security controls to verify detection and response capabilities, including WAF bypass, firewall rule removal, log pipeline disruption, and EDR disablement scenarios using boto3 and subprocess.