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Cloud & Automation: Changing CSPs’ OpEx outlook
Advances in Artificial Intelligence (AI) continue to reshape the security landscape, enabling organizations to minimize their exposure to vulnerabilities. While AI has improved the efficiency of detection and response on a large scale, it comes with new challenges that can only be resolved through hybrid use cases drawing from legacy systems and human-digital interventions.
AI has significantly increased the Application Programming Interface (API) attack surface, raising questions about whether APIs are secure enough when pitted against advanced AI. In the case of Machine Learning (ML) applications and Large Language Models (LLMs), there are security and privacy risks associated with datasets used for training AI models. These issues contribute to a significant uptick in API security risk. At the same time, AI is pushing security teams to become better at threat modeling and work smarter.
AI has immense potential to enhance API security. Here are some exciting applications.
Recent research has shown that AI-driven solutions can reduce incident response times by as much as 51%. For example, Google’s use of Gen AI enables quicker recovery and limits the impact of breaches. While Meta’s AI system for root cause analysis has streamlined the investigation process, which means teams can respond even faster.
By monitoring user behavior and access patterns, AI can adapt access controls in real time and reduce risks. This means organizations can grant access only when necessary and revoke access if suspicious activity is detected.
AI can predict threats and vulnerabilities by looking at patterns in historical data, which is especially important for industries that handle large amounts of sensitive information. In healthcare, for example, Darktrace’s AI enables secure data sharing via APIs and compliance with regulations, all without the need for constant human oversight.
While AI has its benefits, it introduces several new risks to API security that organizations need to understand and manage.
According to Forrester’s “Predictions 2024: Security and Risk,” at least three data breaches will be publicly attributed to ‘insecure AI-generated code’ in 2024. Remember the T-Mobile data breach in 2023 where due to lack of security, 37 million customer’s personal data was exposed. The ChatGPT data breach in March 2023 is another example of how AI-generated code can accidentally leak sensitive information. A bug in the open-source Redis library used by ChatGPT exposed users’ payment details and chat history to other users.
When AI models are trained on flawed data, the output is biased or incorrect and adds another layer of API security risk. For example, the recruiting algorithm Amazon used between 2014 and 2017 was biased against female candidates and would eliminate resumes that had the word “women” or women’s colleges in them.
According to the 2023 State of API Security report by Traceable AI, more than half (52%) of organizations are concerned about financial loss and the threat to Intellectual Property (IP). And 50% of respondents reported a decline in brand value, indicating the long-term damage to a company’s reputation and the multiplier effect on financial impact.
An effective strategy entails leveraging AI while reducing security risks. Security teams can combine AI techniques with traditional security measures to provide a more complete protection plan.
The Mercedes-Benz API breach in the vehicle diagnostic API in 2024 showed us that a balanced approach is critical — it highlights how a combination of AI capabilities like anomaly detection and real-time monitoring and traditional security methods like encryption and strict access controls can limit threats. Organizations must ensure that every interaction with the API is safe, and organizations must regularly update and improve the overall security practices and protocols for the API.
According to Forrester, AI makes social engineering attacks more advanced and challenging to detect. In 2022, attackers used AI to amplify a flaw in Twitter’s API and get unauthorized access to confidential data. Security teams need regular security audits and continuous education and training to understand how to use AI for defense and offense.
While machines can talk to each other through APIs, humans often interact with these systems, which introduces extra risk. Traditional web app attacks and authentication issues are common threats to APIs. The Experian API hack in April 2021 is a perfect example of what happens when organizations do not secure their authentication mechanisms. A one-size-fits-all approach does not cut it.
API security needs an adaptive approach that considers human factors. Layered security with behavioral analytics can detect and mitigate threats based on user behavior, offering more protection for human and machine interactions.
To reap the benefits of AI while dialing down its risks, organizations should adopt a multi-layered security approach. Here are some best practices:
As AI gets more advanced, it will do more for threat detection, response, and prevention. But that also means security teams need to stay one step ahead of the threat actors and adapt continuously. By understanding the opportunities and risks of AI and adopting a comprehensive security strategy, organizations can secure their APIs in the age of AI.
API Creation and API Management from Torry Harris Integration Solutions offers a suite of tools and services to secure APIs from emerging threats. Our expertise in API management helps organizations implement best practices, comply with regulations, and maintain a competitive edge in the fast-changing security landscape.
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