The hype and adoption of AI seems to be at an all-time high, with nearly 70% of respondents in S&P’s recent report on Global AI Trends saying they have at least one AI project in operation. The promise of AI has the potential to fundamentally reshape business operations, but it also creates new risk vectors and opens the door to bad actors that most companies are currently unequipped to mitigate.
3 reports in the last 6 months (S&P Global’s 2023 AI World Trends Report, Foundry’s 2023 AI Priorities Surveyand Forrester reports Security and privacy concerns are the biggest barriers to generative AI adoption) all gave the same results. Data security is the biggest challenge and barrier for organizations looking to adopt and implement generative AI.. The increased interest in implementing AI has directly increased the amount of data that organizations store across cloud environments. Naturally, the more data that is stored, accessed, and processed across different cloud architectures, typically across different geographic jurisdictions, the greater the security and privacy risks.
If an organization does not have the proper protection in place, it quickly becomes a prime target for cybercriminals. According to the Unit 42 2024 Incident Response Report, the rate of data theft is increasing, with 45% of attackers stealing data in less than a year. The day after the compromise. As we enter this new “AI era,” where data is our lifeblood, organizations that understand and prioritize data security will be able to securely pursue all the capabilities that AI has to offer without fear of future repercussions. You will be in the position of
Developing the foundation for an effective data security program
An effective data security program in this new AI era can be broken down into three principles:
- Secure of A.I.: You cannot secure all your AI deployments, including your data, pipelines, and model outputs, on your own. Security programs must consider the context in which AI systems will be used and the implications for sensitive data exposure, effective access, and regulatory compliance. Securing the AI models themselves means identifying model risks, excessive access, and data flow violations throughout the AI pipeline.
- Secure from A.I.: Like most new technologies, artificial intelligence is a double-edged sword. Cybercriminals are increasingly leveraging his AI to generate and execute large-scale attacks. Attackers are now leveraging generative AI to create malicious software, craft convincing phishing emails, and spread disinformation online through deepfakes. It is also possible for attackers to compromise the generative AI tools and large-scale language models themselves. This could result in data leakage or harmful consequences from the affected tools.
- Secure and AI: How can AI become an integral part of your defense strategy? Employing defensive technologies opens up opportunities for defenders to predict, track, and thwart cyberattacks at unprecedented levels. AI provides a streamlined way to sift through threats and prioritize the most important ones, saving security analysts a huge amount of time. AI is also particularly effective at pattern recognition, potentially stopping threats that follow repeated attack chains (such as ransomware) earlier.
By focusing on these three data security disciplines, organizations can confidently explore and innovate with AI without fear of putting their company at risk.
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