In the rapidly evolving world of Artificial Intelligence, enterprises are continuously seeking innovative ways to harness the power of AI for profit and competitive advantage. However, this potential is increasingly being threatened by a new breed of challenges known as runtime inference attacks. These insidious attacks are not just technical nuisances but significant threats that can transform profitable AI deployments into financial liabilities, draining enterprise budgets, derailing regulatory compliance, and ultimately destroying the Return on Investment (ROI) of new AI deployments.

Runtime inference attacks often exploit vulnerabilities in AI models during the inference phase, where the model processes and makes predictions based on new input data. This phase is critical for AI applications, as it directly impacts the accuracy and reliability of the AI system. In essence, these attacks can manipulate the inference process, leading to erroneous predictions or unwanted behavior by the AI system. These not only distort the intended operation of AI systems but also escalate costs due to increased computational requirements and potential fixes necessary after attacks.

Financial impacts are among the most worrying issues for enterprises. Undetected inference attacks could mean that companies are unknowingly investing in flawed AI systems. The increased computational costs from these attacks can rapidly consume budgets allocated for AI, turning these technological investments into budget black holes. Moreover, the need for ongoing vulnerabilities assessments and system patches also adds to operational costs.

Apart from the financial implications, runtime inference attacks have regulatory repercussions. Many industries, particularly those in sectors like healthcare or finance, are subject to stringent regulatory frameworks. An AI system compromised by inference attacks could result in non-compliance issues, exposing companies to legal penalties and reputational damage.

To mitigate these risks, businesses must integrate robust AI safety and monitoring protocols throughout the AI lifecycle. This includes implementing secure model architectures, continuously monitoring for unusual behavior in AI systems, and ensuring all AI applications meet industry compliance standards. Introducing these measures can help preserve the integrity of AI systems, protect enterprise investments, and maintain regulatory compliance.

Furthermore, investing in AI tools specifically designed to detect and prevent inference attacks is wise. These tools provide the necessary defense layer to safeguard AI systems during deployment. Regular training sessions for teams to recognize and respond to these threats quickly should also be part of a comprehensive strategy to combat runtime attacks.

In conclusion, while the promise of AI remains vast, enterprises must remain vigilant against the evolving threat landscape. Effective countermeasures against runtime inference attacks are essential for maintaining financial viability, ensuring compliance, and securing the long-term benefits of AI investments.