On-Chain Analytics + AI
On-chain analytics combined with AI converts blockchain activity into actionable trading signals including arbitrage alerts, profit-taking cues, and risk flags using real-time predictive models.
Suyash Raizada
409 articles published
Prompt Injection and LLM Jailbreaks
Prompt injection and LLM jailbreaks are top LLM security risks in production. Learn the main attack types, real-world examples, and layered defenses for RAG pipelines, agents, and multimodal applications.
Backtesting AI Crypto Trading Strategies: Avoiding Overfitting, Lookahead Bias, and Data Leakage
Learn how to backtest AI crypto trading strategies correctly by avoiding overfitting, lookahead bias, and data leakage, plus walk-forward testing and realistic slippage modeling.
AI Security 101: Core Threats, Attack Surfaces, and Defensive Controls for Modern ML Systems
AI Security 101 guide to core AI threats, key attack surfaces, and defensive controls for modern ML systems, including agent governance, SecDevOps, and monitoring.
AI + Smart Contracts: Automating Decisions Safely with Explainability and Human Oversight
Learn how AI + smart contracts enable adaptive automation while staying safe through explainability, AI-driven audits, real-time monitoring, and human oversight.
Adversarial AI in Cybersecurity: Poisoning, Evasion, and Prompt-Injection Threats (and How to Mitigate Them)
Adversarial AI in cybersecurity includes data poisoning, evasion, and prompt injection. Learn key threats, real-world tactics, and practical mitigation strategies.
Explainable AI for Security: Detecting Attacks, Bias, and Model Drift with Interpretability
Explainable AI for security makes threat detection auditable and trustworthy, helping teams reduce false positives, uncover bias, and detect model drift in SOC and Zero Trust workflows.
Security Metrics for AI: Measuring Robustness, Privacy Leakage, and Attack Surface Over Time
Learn practical security metrics for AI to track robustness, privacy leakage, and attack surface over time using OWASP, MITRE, CI/CD testing, and runtime monitoring.
Ethical Hacking for AI Systems: Step-by-Step Pen-Testing for ML and LLM Apps
Learn a step-by-step ethical hacking methodology for AI systems, including pen-testing ML pipelines and LLM apps for prompt injection, RAG leaks, and tool abuse.
AI Threat Detection in SOCs: Using ML for Anomaly Detection Without Creating New Risks
Learn how AI threat detection in SOCs uses ML anomaly detection to spot unknown threats while managing risks like false negatives, alert fatigue, and over-reliance on automation.
Building an AI Incident Response Plan: Monitoring, Triage, Containment, and Postmortems
Learn how to build an AI incident response plan with monitoring, triage, containment, and postmortems to reduce MTTR, cut false positives, and improve recovery.
AI Data Privacy Compliance
AI data privacy compliance in 2026 blends GDPR, HIPAA, and the EU AI Act with expanding state laws. Learn how to implement inventories, DPIAs, BAAs, and human oversight.