Artificial intelligence (AI) has quickly evolved into an important driver of all business processes. This has made the entire security landscape increasingly complicated and difficult to navigate. Capturing, governing and securing thousands of new AI models will pose significant challenges for every security team. This opens a dangerous hole in your security and compliance posture. As organizations harness AI capabilities, they confront a growing risk landscape, one that traditional security tools are ill-equipped to handle.
Both for profit and nonprofit, organizations are really getting interested in AI. They use open source AI models and libraries, as well as third-party models from platforms like Hugging Face. This reliance on AI raises the bar for security frameworks and standards. These frameworks need to be equipped to address the distinct risks inherent in deploying AI. Traditional lineage tools and first-generation AI Security Posture Management (AI-SPM) solutions don’t help overcome these challenges. As a result, security professionals are finding themselves with large blind spots in their defenses.
The Shortcomings of Traditional Security Tools
Traditional security solutions mostly focus on cloud and SaaS. Regrettably, they frequently fail to acknowledge the deep complexities of the AI landscape. This common oversight leads to very exploitable vulnerabilities that attackers can easily take advantage of. Despite the growing recognition, many organizations are unaware of or underestimating the effects of “shadow AI.” Further, developers and data scientists are deploying unmanaged models, creating a huge compliance and security gap with no security oversight.
Additionally, conventional security solutions lack the necessary visibility across the dynamic AI supply chain. Many organizations are moving quickly to incorporate AI into their work. These tools consistently fail to catch up to the rapid pace and evolution behind modern AI development. Such errant oversight, or lack thereof, could have noxious results from data exfiltration and IP theft to operational disruption. Supply chain breaches on their own have an average cost of $4.5 million to organizations, highlighting the pressing need for more robust security infrastructures.
The Need for an Advanced AI-SPM Framework
To address these challenges, organizations must adopt a truly advanced AI-SPM framework that offers comprehensive visibility into the entire supply chain. Such a framework would require proactive testing procedures to identify and remediate model vulnerabilities as well as reconstruct data lineage for audit and compliance mandates. Reconstructing this model is vital for keeping organizations on the right side of regulatory requirements and for maintaining trust with their customers.
An AI-SPM framework needs to ensure zero trust controls at the inference point. This model protects user data by doing detailed monitoring and validating of every interaction with AI models. Consequently, it is instrumental in minimizing the risk of unauthorized access and exploitation. Agencies can make big strides in strengthening their security posture by adopting these practices. This proactive approach is necessary to protect against the dynamic and emerging threats posed by AI.
Bridging Operational Silos
AI innovation is the catalyst that is removing the operational silos that have traditionally separated cloud, SaaS and endpoint security. This security integration presents a unique opportunity for organizations to advance their security posture. It further incentivizes them to adopt a broader perspective on risk management. AI is quickly eating enterprise data and assets at every turn. Security teams need to develop more holistic strategies that protect throughout the entire ecosystem.
To best protect from the next attack, organizations need to adopt a collective security approach. This framework looks at every part of the AI supply chain. This strategy increases compliance by over 80%. Besides that, it fosters a rich culture of security awareness among developers and data scientists that are being trained to work with AI models.