Continuous Red Teaming Key to Securing AI Models

For nearly two decades, enterprise leaders have trusted VB Transform to tackle the toughest tech challenges. This year’s spotlight is on defending AI models under siege by adversarial cyberattacks.

Recent data shows 77% of enterprises have faced adversarial model attacks, with 41% exploiting prompt injections and data poisoning. Attackers’ tradecraft is evolving faster than classic cyber defenses can keep up.

Why Traditional Defenses Fall Short

Conventional cybersecurity approaches rely on static rules and perimeter controls, which simply aren’t built to handle AI-driven threats. As adversaries develop new attack methods, organizations must move from reactive patches to proactive, lifecycle-long security strategies.

Common Adversarial Attack Types

• Data Poisoning: Adversaries inject corrupted training data, creating persistent model inaccuracies and eroding trust in AI-driven decisions.
• Model Evasion: Carefully crafted inputs exploit limitations of rule-based defenses, slipping malicious data past traditional filters.
• Model Inversion: Systematic queries extract confidential training data, exposing sensitive or proprietary information.
• Prompt Injection: Malicious inputs trick generative AI into bypassing safeguards, generating harmful or unauthorized outputs.
• Dual-Use Frontier Risks: Lowered barriers let non-experts launch sophisticated exploits, from cyberattacks to chemical threats, reshaping the global risk landscape.

Building Continuous Red Teaming into DevSecOps

Leading analysts at Gartner and NIST emphasize continuous threat exposure management (CTEM) as the new standard. Integrating automated adversarial testing, human-in-the-loop reviews, and real-time monitoring into every phase of the SDLC not only uncovers hidden vulnerabilities but also ensures compliance with evolving regulations like the EU’s AI Act.

Five Strategies to Fortify AI Security Now

• Integrate security early in model design and throughout development to catch vulnerabilities before they compound.
• Deploy adaptive, real-time monitoring that leverages AI to detect subtle anomalies and trigger instant response.
• Balance automated adversarial scans with expert human analysis to ensure precise, actionable insights.
• Engage external red teams periodically to expose blind spots and validate internal defenses.
• Maintain dynamic threat intelligence feeds to continuously update defenses against evolving attacker tradecraft.

Red teaming is no longer optional; it’s essential for trust, resilience and compliance. By embedding continuous adversarial testing into your AI lifecycle, you build defences that adapt as attackers evolve.

Join Louis at VB Transform this June to dive deeper into AI red teaming strategies and how top organizations are staying steps ahead of adversaries.