Results for “high-risk-ai”

Implements AI safety guardrails including input validation, output filtering, robustness testing, human oversight, and monitoring to prevent harmful outputs and ensure system reliability.

Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness, providing detailed improvement recommendations with frameworks, testing methodologies, and educational content.

Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks, scanning for controls and generating a compliance report.

Assess AI/ML systems for prompt injection, jailbreak vulnerabilities, model inversion risk, data poisoning exposure, and agent tool abuse, with MITRE ATLAS mapping and guardrail recommendations.

Apply least-privilege tool allowlisting, identity binding, and human-in-the-loop controls for agent tool calls.

Proactively hunts for Advanced Persistent Threat activity using hypothesis-driven searches across endpoint telemetry, network logs, and memory artifacts.

Scan and audit AI agent skills for security risks before installation, producing a PASS/WARN/FAIL verdict with findings and remediation guidance.

Conduct authorized security assessments of LLM applications and AI agents, covering prompt injection, tool abuse, RAG exposure, memory poisoning, and model supply-chain risks.

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

Guides AI system compliance with the EU AI Act through six article-cited questions covering prohibited practices, risk classification, conformity assessment, role obligations, transparency, and GPAI systemic risk.

Automates multi-source enrichment of IPs, domains, URLs, and file hashes using VirusTotal, AbuseIPDB, Shodan, GreyNoise, URLScan.io, and MISP to provide contextual risk scoring and disposition recommendations for SOC analysts.

Safety guardrails for autonomous agents. Intercepts destructive actions and vets skills, requires structured approval before irreversible operations, with CLI-backed cryptographic audit trails and policy enforcement.

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