… is falling behind
AI applications are exposed to entirely new types of attacks and therefore require new and different protection measures in addition to traditional security methods.
Source: Forrester
Source: Gartner
Source: McKinsey & Company
The rapid advancement of artificial intelligence offers tremendous opportunities, but it also introduces significant risks. One of the greatest challenges is implementing AI securely without creating new security vulnerabilities or weaknesses. Protecting sensitive data and preventing cyberattacks and unauthorized AI manipulation are especially critical.
In einer Welt, in der künstliche Intelligenz zunehmend unser tägliches Leben und Geschäftsprozesse prägt, ist es unerlässlich, die Nutzung effektiv abzusichern. Ohne ausreichende Schutzmaßnahmen sind sensible Daten, ethische Standards und Compliance gefährdet, was nicht nur finanzielle, sondern auch rechtliche und reputationsbezogene Risiken birgt. Eine abgesicherte KI-Nutzung stärkt Vertrauen, minimiert Risiken und ermöglicht verantwortungsvolle Innovation – entscheidend, um langfristig wettbewerbsfähig zu bleiben.
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AI applications are exposed to entirely new types of attacks and therefore require new and different protection measures in addition to traditional security methods.
How is data processed in an AI context? Questions of transparency and accountability often remain unanswered, especially when using third-party AI models.
Using AI with sensitive customer data or code can lead to data leaks. Employee training and clear policies are essential.
Security and compliance teams must address new risks but often lack the necessary methods, resources, or standards to deal with AI-specific threats.
.png&w=3840&q=75)
AI applications are exposed to entirely new types of attacks and therefore require new and different protection measures in addition to traditional security methods.
How is data processed in an AI context? Questions of transparency and accountability often remain unanswered, especially when using third-party AI models.
Using AI with sensitive customer data or code can lead to data leaks. Employee training and clear policies are essential.
Security and compliance teams must address new risks but often lack the necessary methods, resources, or standards to deal with AI-specific threats.
.png&w=3840&q=75)
AI applications are exposed to entirely new types of attacks and therefore require new and different protection measures in addition to traditional security methods.
How is data processed in an AI context? Questions of transparency and accountability often remain unanswered, especially when using third-party AI models.
Using AI with sensitive customer data or code can lead to data leaks. Employee training and clear policies are essential.
Security and compliance teams must address new risks but often lack the necessary methods, resources, or standards to deal with AI-specific threats.
Clearly defined strategies and organizational structures create the framework for the secure use of AI across the entire company. They regulate responsibilities and, through tailored controlling of key metrics, enable transparency and effective governance.
Embedding AI security early in the development process—through qualified teams, clear standards, and consistent approaches to identifying and reducing risks. Complementary technical security measures and corresponding capabilities ensure an appropriate level of protection.
Secure, controlled, and auditable operation, as well as responsible use of AI, supported by clear processes, monitoring, and user enablement.
Customised advice tailored to your strategic goals and regulatory requirements.
Wir identifizieren relevante KI-Anwendungsfälle, analysieren Risiken und Schwachstellen und entwickeln priorisierte Sicherheitsmaßnahmen. Diese können über Pilotprojekte und individuelle Playbooks umgesetzt werden. Ergebnis sind dokumentierte Risikoanalysen, Maßnahmenkataloge und Roadmaps zur Operationalisierung.
Nach dem Model Sourcing und vor dem Deployment ermöglicht KI Modell Scanning, sicherheitsrelevante Schwachstellen, Backdoors oder schädliche Payloads in den ausgewählten Modellen frühzeitig zu erkennen, bevor Schäden eintreten können.
KI-Governance gewährleistet den verantwortungsvollen, effizienten und sicheren Einsatz von KI, minimiert dabei zahlreiche Risiken und stärkt das Vertrauen sowie die Transparenz im Umgang mit KI.
CART deckt Schwachstellen in hochdynamischen und komplexen Systemen frühzeitig auf. Als anbieterunabhängiger Implementierungspartner unterstützen wir unsere Kunden von der Auswahl eines Tools, über die Umsetzung bis zur Produktivsetzung.
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