Results for “detection”

Scans local repos and AI agent skill folders for security drift, saves trusted baselines, and compares changes after updates to highlight risky new capabilities.

Analyze text and images for harmful content using Azure AI Content Safety SDK for Java. Supports hate, violence, sexual content, and self-harm detection with blocklist management.

Generates AI-ready context files for any codebase by auto-detecting the tech stack, scoring AI-readiness, and producing a portable .faf file that works across AI tools.

Build, deploy, evaluate, debug, and measure latency for a GPU-accelerated FastAPI service that detects whether operators perform assembly-line steps in order via event boundary detection and VLM classification.

Audits Kubernetes pods for container escape vectors by analyzing privileged mode, dangerous capabilities, host namespace sharing, and writable hostPath mounts using the Kubernetes Python client.

Analyze proxy logs, DNS query logs, and netflow data to detect unauthorized SaaS and cloud service usage, classify domains, and flag high-risk services.

Monitors cloud workloads for runtime threats by checking process lists, network connections, file integrity, and resource utilization anomalies on EC2 and GCE instances.

Deploy Llama Guard, NeMo Guardrails, and LLM Guard as runtime input/output scanners to block jailbreaks, prompt injection, and toxic content in production LLM applications.

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.

Security controls for RAG. Indirect prompt-injection via retrieved documents, PII detection/redaction (Microsoft Presidio, AWS Comprehend), multi-tenant isolation, ACL-aware retrieval with row-level/metadata filtering, data-leakage prevention, jailbreak hardening on retrieved context, GDPR right-to-be-forgotten in vector DBs. USE WHEN: user mentions "prompt injection RAG", "indirect prompt injection", "PII redaction", "Presidio", "ACL RAG", "row-level security", "multi-tenant RAG isolation", "GDPR vector DB", "right to be forgotten", "jailbreak", "data leakage RAG" DO NOT USE FOR: hallucination detection - use `rag-guardrails`; tenancy scaling patterns - use `rag-production`; audit tracing schema - use `rag-observability`

Runs a three-layer verification pipeline on AI-generated output: extracts verifiable claims, finds supporting or contradicting sources via web search, and produces a structured verification report with source links for human review.

Deploys and monitors ransomware canary files across critical directories using Python's watchdog library for real-time filesystem event detection, triggering alerts via email, Slack, or syslog when decoy files are accessed.

Deploys and configures Suricata IDS/IPS with Emerging Threats rulesets, EVE JSON logging, and custom rules for real-time network traffic inspection, threat detection, and integration with SIEM platforms.

Deploy AI and NLP-powered detection systems to identify business email compromise attacks by analyzing writing style, behavioral patterns, and contextual anomalies that evade traditional rule-based filters.

Detect prompt injections and jailbreak attempts in LLM applications using Meta's 86M parameter classifier. Filter user inputs, third-party data, and RAG documents with low latency and multilingual support.

Detects and responds to OAuth token theft and replay attacks in cloud environments, focusing on Microsoft Entra ID token protection, conditional access policies, and sign-in anomaly detection.

Query AWS CloudTrail events with boto3, build statistical baselines of normal API activity, and detect anomalies such as unusual event sources, geographic anomalies, high-frequency API calls, and first-time API usage patterns.

Deploy Runtime Application Self-Protection (RASP) agents to detect and block attacks from within application runtime, covering OpenRASP integration, attack pattern detection, and security policy configuration for Java and Python web applications.

Builds input and output validation guardrails for LLM-powered applications to prevent prompt injection, data leakage, toxic content generation, and hallucinated outputs using NeMo Guardrails, Presidio, and Guardrails AI.

Automates the full DEFT AOI improvement loop for NVIDIA TAO VisualChangeNet / ChangeNet PCB inspection models, including baseline evaluation, RCA, synthetic defect generation, data mining, retraining, and deployment gating until KPI targets are met.

Detect compromised cloud credentials across AWS, Azure, and GCP by analyzing anomalous API activity, impossible travel patterns, unauthorized resource provisioning, and credential abuse indicators using GuardDuty, Defender for Identity, and SCC Event Threat Detection.

Establishes recurring vulnerability scanning workflows using Nessus, Qualys, or OpenVAS, prioritizes findings with risk scoring and CISA KEV data, integrates with SIEM for exploitation detection, and tracks remediation via SLA-based dashboards and automated ticketing.

Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs using pandas for behavioral analytics and statistical baselines.

Scan source code repositories, CI/CD pipelines, and configuration files for exposed AWS credentials using TruffleHog, git-secrets, and AWS-native detection mechanisms to prevent credential theft and unauthorized account access.

Detect lateral movement in Azure AD/Entra ID environments using Microsoft Graph API audit logs, Azure Sentinel KQL hunting queries, and sign-in anomaly correlation to identify privilege escalation, token theft, and cross-tenant pivoting.

Detects all languages used on a webpage — both declared (html@lang, hreflang, nested lang=, meta content-language) and actually present in the body text using Google CLD3 WASM. Reconciles the two signal sets and flags mismatches for i18n audits, hreflang validation, and multilingual content verification.

Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls, and potential data exfiltration using statistical baselines.

Unity XR — XR Interaction Toolkit 3.x (Interactor/Interactable/Manipulation), AR Foundation (planes, anchors, image tracking, body tracking), OpenXR, hand tracking on Quest/Vision OS. USE WHEN: VR/AR/MR projects, integrating XR controllers, AR plane detection, XR locomotion, hand tracking, eye tracking, OpenXR features. DO NOT USE FOR: regular 3D gameplay (use `unity-physics-anim`); rendering pipeline (use `unity-rendering` — XR has URP-specific considerations).