Directory
AI Security Skills
10 skills at the intersection of artificial intelligence and cybersecurity
AI-Powered Threat Detection
AI-powered threat detection applies machine learning models to security telemetry to identify malicious activity in real time. Unlike rule-based detection, ML models surface anomalous patterns and unknown threats that traditional signatures miss entirely. As adversaries evolve faster than manual rule updates allow, this skill is increasingly essential for modern security operations teams.
LLM Security Assessment
LLM security assessment involves evaluating large language model deployments for vulnerabilities specific to generative AI, including prompt injection, data leakage, insecure output handling, and model manipulation. As organizations integrate LLMs into products and internal workflows, assessing their security posture is a critical and rapidly growing discipline. Practitioners must understand both the AI technology and classic application security principles.
AI-Assisted Penetration Testing
AI-assisted penetration testing leverages machine learning and large language models to augment traditional manual pentesting workflows. AI tools accelerate reconnaissance, suggest attack paths, generate payloads, and analyze results at scale. This skill combines solid offensive security fundamentals with proficiency in AI-powered tooling to increase coverage and velocity.
ML Anomaly Detection
Machine learning anomaly detection builds statistical models of normal behavior across networks, endpoints, and users, then flags deviations as potential threats. This approach excels at catching insider threats, lateral movement, and novel malware that signature-based tools miss. Tuning these models to reduce alert fatigue while maintaining sensitivity is a key practitioner skill.
AI Red Teaming
AI red teaming involves systematically attacking AI systems to discover vulnerabilities before adversaries do. This includes testing LLMs, ML pipelines, and AI-integrated products for prompt injection, model evasion, data poisoning, and unintended behaviors. It is one of the fastest-growing disciplines in cybersecurity as AI systems proliferate across enterprise environments.
Generative AI Security Review
Generative AI security review assesses the security posture of applications and workflows built on GenAI technologies. This includes reviewing system prompts, API configurations, output handling, user trust boundaries, and data flows for risks specific to LLM-powered systems. Organizations deploying GenAI internally or in products need this review before and after go-live.
AI Supply Chain Security
AI supply chain security covers securing the end-to-end pipeline of AI development, from training data sourcing to model deployment and inference. Threats include poisoned training data, malicious model weights distributed through public registries, compromised ML dependencies, and insecure model serving infrastructure.
Adversarial ML Defense
Adversarial ML defense focuses on making machine learning models robust against inputs specifically crafted to fool them. This is especially critical for ML models used in security decisions — such as malware classifiers, fraud detectors, and network intrusion detection — where successful evasion has severe downstream consequences.
AI-Driven SOC Automation
AI-driven SOC automation uses machine learning and large language models to automate repetitive analyst tasks, accelerate triage, and improve detection quality in security operations centers. The goal is reducing alert fatigue so human analysts can focus on complex investigations requiring contextual judgment.
NLP for Phishing Detection
Natural Language Processing techniques are applied to email content, URLs, and web pages to detect phishing with high accuracy. NLP models identify deceptive language patterns, brand impersonation, and social engineering tactics that simple rule-based filters miss. This skill bridges ML engineering and email security operations.
No skills found at this difficulty level.