Wisr AI Systems and Moneylab Enter into an MOU to Develop Agentic AI Cyber Platform Focused on Financial Services Risk
Security research shows an AI‑trained malware (using Qwen 2.5 LLM) can bypass Microsoft Defender ~8% of the time, while SRAM-trained variants struggle to reach 1%. Developed in just three months on a $1,600 budget, it demonstrates how reinforcement‑learning enables automated creation of evasive malware at scale. With Black Hat 2025 slated to expose its inner workings, this marks a major escalation in cybersecurity: adversaries are now using AI to automate and optimize attack craft. Responding demands higher fidelity detection models and adversarial ML defenses.
Wisr AI Systems Announces Alpha Launch of RiskAssure: Integrating Agentic AI for Instant Third-Party Risk and Compliance Analysis
Security research shows an AI‑trained malware (using Qwen 2.5 LLM) can bypass Microsoft Defender ~8% of the time, while SRAM-trained variants struggle to reach 1%. Developed in just three months on a $1,600 budget, it demonstrates how reinforcement‑learning enables automated creation of evasive malware at scale. With Black Hat 2025 slated to expose its inner workings, this marks a major escalation in cybersecurity: adversaries are now using AI to automate and optimize attack craft. Responding demands higher fidelity detection models and adversarial ML defenses.
Wisr CEO CEO to Speak at 2025 Information Technology Symposium on Agentic AI and Third-Party Risk Management
Security research shows an AI‑trained malware (using Qwen 2.5 LLM) can bypass Microsoft Defender ~8% of the time, while SRAM-trained variants struggle to reach 1%. Developed in just three months on a $1,600 budget, it demonstrates how reinforcement‑learning enables automated creation of evasive malware at scale. With Black Hat 2025 slated to expose its inner workings, this marks a major escalation in cybersecurity: adversaries are now using AI to automate and optimize attack craft. Responding demands higher fidelity detection models and adversarial ML defenses.
