The following solution approaches are often preceded by a chorus of established resistance, a litany of impossibilities:
"THAT'S NOT FEASIBLE!", "FAR TOO RADICAL!", "WE'VE ALWAYS DONE IT THIS WAY!", "PURE THEORY, THAT'S NOT PRACTICAL!", "OUR ENTIRE SYSTEM WOULD HAVE TO BE CHANGED FOR THAT!", "THAT DOESN'T CONFORM TO ANY NORM!", "NOT VERIFIED AND THEREFORE UNSAFE BY DEFINITION!"
And not infrequently, skepticism culminates in fundamentally questioning the source: "SUGGESTIONS FROM A BLOG? THAT'S HARDLY A SERIOUS BASIS FOR DECISIONS OF SUCH MAGNITUDE!".
Such defensive reflexes are often the first line of defense for traditional thought patterns and established comfort zones against the truly new and potentially disruptive. However, the urgency and unique nature of the AI challenges addressed here demand more than mere continuation of the familiar. They demand the courage to break existing paradigms and to tread fundamentally different paths. What is ultimately needed is not just the theoretical will to change, but the practical insight into its absolute necessity.
PS: Put your external consultants out of a job and read these proposals.
Conclusion: "Anyone who wants to secure AI like an online shop hasn't understood the problem."
My Solution Approaches:
Chapter 21.1: The New Boundary Logic: An API Model - Link: HTML Version
Chapter 21.2: Text Crypter: Encryption as Default, Not as an Option - Link: HTML Version
Chapter 21.3: The Semantic Output Shield: How to Secure AI Outputs - Link: HTML Version
Chapter 21.4: The Self-Learning AI - Link: HTML Version
Chapter 21.5: Countermeasures in the Learning Security Core: The Architecture of Resilience - Link: HTML Version
Chapter 21.6: Context as a Vulnerability: Architectural Responses to Semantic Poisoning - Link: HTML Version
Chapter 21.7: Multimodal Pre-Check (Input Sandbox) - Link: HTML Version