Government agencies are under immense pressure to modernize, both from the legislature and constituents demanding a digital-first experience. Advances in autonomous AI agents open new opportunities to leverage Software as a Service (SaaS) to improve the cost effectiveness, efficiency, and quality of government operations and services. To take full advantage of the power of agentic Artificial Intelligence (AI), agencies must satisfy their part of the SaaS shared responsibility model to properly protect their data. SaaS security is not only essential to defend against increasing threats and to comply with government mandates – AI agents depend on the confidentiality, availability, and integrity of data.
For example, when relevant data is not available for developing or delivering AI agents, the results will be incomplete or error-prone. If an organization does not detect that data has been corrupted, and inadvertently includes it in the development or use of AI agents, this undermined integrity of information can pollute the outputs (garbage in, garbage out). If sensitive data is not properly restricted or anonymized, it could be exposed through an AI agent inadvertently, or even intentionally by adversarial prompts, compromising confidentiality.
Enhancing Protection
Security products that take an outside-in API-based approach are necessary but not sufficient for securing SaaS data. Robust SaaS data security requires a data-centric bottom-up approach that includes data classification, risk prioritization, and rapid remediation. However, agencies have difficulty performing these data security tasks due to a combination of increasing volumes of data, more work to do than resources available, and a shortage of SaaS security expertise. Fortunately, these are challenges that agentic AI is ideally suited to alleviate. AI agents leverage conversational AI to empower users and other AI agents with quick and easy access to consolidated and contextually relevant security information. This eliminates the need for deep security expertise, enabling users to find answers to their questions effortlessly. Furthermore, by proactively recommending remediation steps and even automating certain workflows, AI agents can streamline security processes and enhance overall efficiency. Government agencies can benefit from solutions that accelerate labeling sensitive data, automatically surface risk-prioritized vulnerabilities, proactively detect drift from secure baseline configurations, and streamline remediation with the help of autonomous AI agents. Additionally, organizations have the unique ability to use AI agents to strengthen security and provide trusted data for other AI agents.
Detecting Threats
There is a rapid rise in the frequency, scale, and sophistication of threats impacting SaaS data, resulting in unauthorized access and destruction of data. The risks and impacts of data loss and corruption due to human error are also increasing. AI-enabled attacks amplify the threats, and growing complexity and data consumption of AI elevate the risks. To keep pace with these accelerating trends, AI-driven alerting is needed to detect problems that may compromise the confidentiality, availability, and integrity of SaaS data that AI depends on. Delivering reliable, actionable alerts allows autonomous AI agents to deal with dangerous actions, improving security and resilience, and reducing the disruption, cost, and impact of SaaS data security incidents.
Incident Response and Recovery
Security incident response and data recovery readiness are key components of resilient SaaS data. Effective response and recovery from such problems depends on a combination of well-defined processes, properly prepared personnel, and fit-for-purpose technology. Recovering SaaS data from backups can be challenging due to the intricate relationships between objects and the need to restore data into a live environment without disrupting ongoing operations. Fit-for-purpose technology can precisely target and restore only the lost or corrupted data at the field level, leaving the bulk of “still good” data untouched. Periodic incident response and data recovery drills elevate an organization’s ability to restore normal operations quickly when an actual problem occurs, and are a regulatory requirement for many agencies.
Leveraging AI Agents
Autonomous AI agents can assist organizations with ongoing SaaS data protection, including risk management and threat detection, increasing trust in and resilience of data. The faster organizations can leverage AI agents to enhance SaaS data security, the more time and opportunity they have to develop new agentic applications. These SaaS data security practices powered by automation and AI can help agencies avoid cyber attacks and compliance violations, and derive numerous operational benefits, such as better utilization of developer resources and enhanced ROI.
We’ve seen firsthand how AI implementation within government agencies offers numerous advantages. While enhanced data protection is a fundamental requirement, the integration of AI agents improves constituent services, empowers employees, and boosts operational efficiency. Moreover, through automation that provides personnel with hands-on experience fostering a practical understanding of AI concepts, they are equipped with the necessary AI skills to manage large datasets. This prepares them for the demands of a digital-first workforce, ultimately improving the quality, efficiency, and usability of government services.
The Future of AI Agents
While SaaS providers deliver a powerful, secure platform and autonomous AI agents, government agencies must understand their role in the shared responsibility model to implement and maintain necessary security controls to protect sensitive information. Against an evolving threat landscape, failure to protect SaaS data not only causes damage and disruption to normal operations, it also slows down AI innovation. Agencies need to prioritize SaaS data security in their digital transformation efforts to ensure successful AI usage and associated benefits.
Such trusted and resilient data is essential for successful applications of AI, which require accessible, reliable, relevant, and error-free data. AI-driven SaaS data security plays a key role in fueling innovation, freeing up time so that cloud specialists can focus on development of high-value projects, including leveraging autonomous AI agents.
About the Author
Dr. Eoghan Casey is the Business Consultant at Salesforce, advancing technology solutions and business strategies to protect SaaS data, including AI-driven threat detection, incident response, and data resilience. With 25+ years of technical leadership experience in the private and public sectors, he has contributed to expertise and tools that help thwart and investigate cyber attacks and insider threats. As Chief Scientist of the DoD Cyber Crime Center (DC3), he was responsible for innovation, strategic collaborations, and advancing standards and practices related to digital forensics, incident response, and malware/CTI analysis. He is on the Board of DFRWS.org, is cofounder of the Cyber-investigation Analysis Standard Expression (CASE) and has a PhD in Computer Science from University College Dublin. Eoghan can be reached online at https://www.linkedin.com/in/caseite/and at our company website www.salesforce.com .
