{"id":46964,"date":"2022-10-14T13:45:23","date_gmt":"2022-10-14T11:45:23","guid":{"rendered":"https:\/\/rertech.it\/?p=46964"},"modified":"2026-08-19T13:28:18","modified_gmt":"2026-08-19T11:28:18","slug":"ai-security-standards-key-frameworks-for-2026","status":"publish","type":"post","link":"https:\/\/rertech.it\/en\/2022\/10\/14\/ai-security-standards-key-frameworks-for-2026\/","title":{"rendered":"AI Security Standards: Key Frameworks for 2026"},"content":{"rendered":"

\"AI<\/p>\n

Prioritizing risks The combination of likelihood and impact assessments forms the basis for prioritizing risks and informs the development of Risk Treatment decisions. These attacks could manipulate creditworthiness assessments, leading to incorrect loan decisions. A financial institution\u2019s AI system, which assesses loan applications using public credit scores, is exposed to data poisoning attacks. Regarding model behaviour, we focus on manipulation by attackers, as the scope of this document is security.<\/p>\n<\/p>\n