"Artificial Intelligence has scaled phishing from manual tasks into high-velocity threats. By automating reconnaissance, generating realistic deepfakes, and optimizing delivery, AI enables even low-skilled actors to execute sophisticated social engineering."
"To regain control, governance must be integrated into the delivery pipeline using model registries, automated security scanning, and unified observability dashboards."
"Securing data integrity from ingestion to inference is critical for long-term accuracy and safety, as subtle changes in training data can cause models to misbehave in unpredictable ways."
"Organizations must develop comprehensive responsible AI frameworks that prioritize fairness, transparency, ethical practices, and compliance with evolving regulations like GDPR and the EU AI Act."
AI has revolutionized phishing, enabling low-skilled actors to execute sophisticated attacks through automation and deepfakes. Governance in AI is crucial to mitigate risks from unregulated API calls and Shadow AI. Training data manipulation poses significant threats, as seen in real-world incidents like Microsoft's Tay chatbot. Organizations must implement robust MLOps practices and responsible AI frameworks to ensure fairness, transparency, and compliance with regulations. Security engineers must adapt to the evolving landscape of AI threats to maintain effective defenses.
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