Results for “bot-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.

Detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using the Resemble AI platform.

Hunt for spearphishing campaign indicators across email logs, endpoint telemetry, and network data to detect targeted email attacks.

Audit a codebase, UI, or copy for machine-generated tells across prose, visual/UI, code, and structure/IA, then produce a phased de-slop burndown. Use when the user says "feels AI-generated", "looks like AI slop", "reads like ChatGPT", "feels generic/soulless", or wants an authenticity/voice pass before launch.

Investigate tokens, wallets, and on-chain entities with natural-language answers backed by on-chain data. Detect insiders, whales, pump-and-dump, wash trading, and wallet networks on Base.

Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features and classifying samples with machine learning models.

Detect brand impersonation attacks across domains, social media, mobile apps, and dark web channels to identify phishing campaigns, fake sites, and unauthorized brand usage.

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.

Use to design and audit how customers are told they are interacting with AI, or that AI was involved in a decision about them, and to evidence that it happened. Trigger for "do we tell customers it's a bot", "AI disclosure requirements", "should the bot say it's not human", "customer asked if they were talking to a person", AI transparency obligations, or evidencing that an AI-assisted decision was explained.

Audit and rewrite content to remove AI writing patterns ("AI-isms"). Use this skill when asked to "remove AI-isms," "clean up AI writing," "edit writing for AI patterns," "audit writing for AI tells," or "make this sound less like AI." Supports a detection-only mode that flags patterns without rewriting.

Proactively hunt for threats by analyzing IOCs, detecting behavioral anomalies in telemetry, and prioritizing signals mapped to MITRE ATT&CK.

Creates vendor-agnostic detection rules using the Sigma rule format for threat detection across SIEM platforms including Splunk, Elastic, and Microsoft Sentinel.

Scans a codebase for security, database, deployment, code quality, AI/LLM, dependency, frontend, and observability issues before deployment. Intercepts deploy commands and blocks until critical items pass.

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