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Tuesday 04 August 2026 15:38:51 GMT

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🫣 :

2026-08-05 02:14:08

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мафтуна :

шунака хийонотларни корсам юрагим огрийдичи

2026-08-05 04:23:59

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2026-08-05 14:03:50

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2026-08-05 14:03:35

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❤️🇺🇸 JAHONGIR 777 🇮🇱❤️ :

2026-08-04 22:24:25

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❤АФСОНА❤ :

🥺

2026-08-04 21:43:32

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Шохиста Юунусова :

2026-08-05 03:55:32

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🔥 Адиз🔥Шах🔥 :

2026-08-05 02:29:37

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люблю свою семью ❤️❤️❤️ :

🤔🤔🤔

2026-08-05 16:00:27

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2026-08-05 07:50:03

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😭😭😭😭😭🥹

2026-08-05 07:05:43

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2026-08-05 07:57:26

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#👍👍 #یک روزی خیلی#به کنار ی #عزیزانم#زنده_باد_افغانستان_متحد_با_تمام_اقوام #پشتون_تاجیک_هزاره_ازبک_زنده_باد🇦🇫 #🇦🇫🇦🇫🇦🇫##🇩🇪🇩🇪#

#👍👍 #یک روزی خیلی#به کنار ی #عزیزانم#زنده_باد_افغانستان_متحد_با_تمام_اقوام #پشتون_تاجیک_هزاره_ازبک_زنده_باد🇦🇫 #🇦🇫🇦🇫🇦🇫##🇩🇪🇩🇪#

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Replying to @rio430704 AI security is becoming one of the most important specialization areas in modern DevSecOps, and the Certified AI Security Professional program by Practical DevSecOps is built around hands on, adversarial learning for real AI systems rather than abstract theory or multiple choice style knowledge. The certification is structured as a lab heavy program that focuses on how AI systems actually break in production and how they are defended. It covers large language model architecture fundamentals, retrieval augmented generation systems, and how AI models are trained and deployed in real pipelines. A major focus is on understanding and exploiting vulnerabilities such as prompt injection, insecure output handling, training data poisoning, model theft, and excessive agent permissions. It also goes deep into AI specific threat modeling using STRIDE methodology and mapping risks through frameworks like MITRE ATLAS and OWASP LLM Top 10. You learn how to identify attack surfaces in AI applications, model pipelines, and APIs, and how those weaknesses are exploited in real environments. A significant portion of the program is dedicated to AI supply chain security. This includes dependency attacks, poisoned models, malicious package injection, model signing, SBOM generation, and securing AI build and deployment pipelines using DevSecOps principles. You also work with defenses like AI firewalls, prompt sanitization, static and dynamic analysis of models, and security controls for agent based systems. On the DevOps side, it covers integrating security into CI CD pipelines for AI systems, scanning and hardening AI workloads, and implementing monitoring and validation techniques for deployed models. The labs are designed around realistic scenarios such as compromised models, vulnerable LLM integrations, and insecure plugin or tool use in agentic systems. There is not any real world demand yet for the certification name itself, meaning employers are not explicitly asking for it. However, the practical nature of the exam is the real value. It forces you to actually build, break, and defend AI systems in controlled environments, which is what ultimately develops usable, job ready skills. At the senior level in AI security and frontier labs, compensation ranges from 150k to 1.28 Million depending on scope, research depth, and infrastructure ownership. After completing this certification, you are aligned with roles such as AI Security Engineer, LLM Security Engineer, DevSecOps Engineer for AI systems, AI Red Team Engineer, Machine Learning Security Specialist, AI Infrastructure Security Engineer, and Security Researcher focused on adversarial machine learning. The key takeaway is simple. The certification itself is not the signal. The skills you build inside it are. #ai #cybersecurity #blackintech #fypシ #xyzbca

Replying to @rio430704 AI security is becoming one of the most important specialization areas in modern DevSecOps, and the Certified AI Security Professional program by Practical DevSecOps is built around hands on, adversarial learning for real AI systems rather than abstract theory or multiple choice style knowledge. The certification is structured as a lab heavy program that focuses on how AI systems actually break in production and how they are defended. It covers large language model architecture fundamentals, retrieval augmented generation systems, and how AI models are trained and deployed in real pipelines. A major focus is on understanding and exploiting vulnerabilities such as prompt injection, insecure output handling, training data poisoning, model theft, and excessive agent permissions. It also goes deep into AI specific threat modeling using STRIDE methodology and mapping risks through frameworks like MITRE ATLAS and OWASP LLM Top 10. You learn how to identify attack surfaces in AI applications, model pipelines, and APIs, and how those weaknesses are exploited in real environments. A significant portion of the program is dedicated to AI supply chain security. This includes dependency attacks, poisoned models, malicious package injection, model signing, SBOM generation, and securing AI build and deployment pipelines using DevSecOps principles. You also work with defenses like AI firewalls, prompt sanitization, static and dynamic analysis of models, and security controls for agent based systems. On the DevOps side, it covers integrating security into CI CD pipelines for AI systems, scanning and hardening AI workloads, and implementing monitoring and validation techniques for deployed models. The labs are designed around realistic scenarios such as compromised models, vulnerable LLM integrations, and insecure plugin or tool use in agentic systems. There is not any real world demand yet for the certification name itself, meaning employers are not explicitly asking for it. However, the practical nature of the exam is the real value. It forces you to actually build, break, and defend AI systems in controlled environments, which is what ultimately develops usable, job ready skills. At the senior level in AI security and frontier labs, compensation ranges from 150k to 1.28 Million depending on scope, research depth, and infrastructure ownership. After completing this certification, you are aligned with roles such as AI Security Engineer, LLM Security Engineer, DevSecOps Engineer for AI systems, AI Red Team Engineer, Machine Learning Security Specialist, AI Infrastructure Security Engineer, and Security Researcher focused on adversarial machine learning. The key takeaway is simple. The certification itself is not the signal. The skills you build inside it are. #ai #cybersecurity #blackintech #fypシ #xyzbca

Me cuesta tanto olvidarte 🥹 #clasicos #mecano #tops

Me cuesta tanto olvidarte 🥹 #clasicos #mecano #tops

Ahora se sumó Tala también 😭😭🤣🤣

Ahora se sumó Tala también 😭😭🤣🤣

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