Moderated by Moderated by Niyati Chhaya, Co-founder at Hyperbots
Niyati Chhaya: Hi everyone, good morning, good evening, and good afternoon. This is Niyati Chhaya. I am a co-founder and I lead AI at Hyperbots. I am thrilled to have Mike with us today. Mike Vaishnav is a CFO, Consultant, and Strategic Advisor to many privately owned organizations. We’re going to pick his brains on AI and compliance. But before that, Mike, why don’t you introduce yourself?
Mike Vaishnav: Thank you, Niyati, it’s a pleasure to be here. I’ve been working in Silicon Valley for about close to 30 years in diverse industries. I’ve had the opportunity to touch each and every aspect of finance, from the controllership role to FP&A, treasury, tax, investor relations, and more. In my last two roles, I also managed operations departments like HR, IT, facilities, legal, and procurement. So, I bring a broad range of experience in finance and operations.
Niyati Chhaya: Wow, I think you are the right person to talk about finance, compliance, and how AI will help in compliance. How do you think AI will assist in compliance?
Mike Vaishnav: AI can significantly enhance compliance. With the right algorithms, AI can flag issues related to government and regulatory requirements. By identifying violations early, companies can address problems before they escalate. AI helps ensure that businesses are operating within the bounds of policies and regulations.
Niyati Chhaya: Got it, and I assume AI can help with fraud detection as well?
Mike Vaishnav: Absolutely, fraud detection is a critical area where AI can be very effective. By monitoring data and raising flags at appropriate levels, AI can help mitigate fraud and ensure compliance with regulatory requirements.
Niyati Chhaya: Do you think AI can play a role in the audit process?
Mike Vaishnav: Definitely. AI can analyze vast amounts of data and identify specific patterns and exceptions. For example, AI can track journal entry approvals, identifying who processed and approved entries. This can streamline the audit process, allowing auditors to focus on more value-added functions rather than spending time on sample tests and data checks.
Niyati Chhaya: Do you think AI can ensure the accuracy of financial reporting?
Mike Vaishnav: Yes, AI can enhance the accuracy of financial reporting. While ERP systems are robust, there are instances where information might be missed. AI can identify these gaps early by using predefined rules and algorithms. For example, when generating financial statements, AI can ensure that all relevant chart accounts are included, reducing the risk of errors. Additionally, AI can assist in compiling and analyzing data for SEC filings, providing insights that ERP systems might not offer.
Niyati Chhaya: Thanks for those insights. My takeaway here is that building reliable AI systems can greatly benefit compliance processes.
Mike Vaishnav: Absolutely. Compliance is crucial for all finance professionals. With AI ensuring compliance, finance teams can rest easy, knowing that they have a reliable system monitoring their processes. AI provides detailed analytics, helping to maintain and improve compliance.
Niyati Chhaya: Got it. Thank you so much, Mike, for sharing your expertise and insights on AI and compliance.
Mike Vaishnav: My pleasure.
Financial process automation is the use of artificial intelligence (AI) to perform various tasks that would otherwise require human intervention, such as data entry, invoice processing, reconciliation, reporting and more. By automating these tasks, businesses can save time, reduce errors, improve efficiency and enhance customer satisfaction.
However, automation also comes with its own set of challenges and risks, especially when it comes to security. The bots that execute the tasks on behalf of or assuming the role of a human user need to be carefully designed, monitored and controlled. A SaaS-based automation solution, must implement a zero-trust environment, where the bots are also treated just like human users, for the very reason that the bots assume the role of a human user for executing the tasks.
Zero-trust security is a principle that assumes that no entity, whether internal or external, is trustworthy by default. It requires verifying the identity and permissions of every user and device before granting access to any resource or data. It also requires monitoring and auditing all activities and transactions to detect and prevent any malicious or unauthorized behavior.
Zero-trust security is especially important for financial process automation, as it involves sensitive and confidential data that needs to be protected from cyber attacks, data breaches, fraud and compliance violations. By applying zero-trust security, the bots are provided with just enough permissions to perform their tasks, and that they are not compromised or misused by hackers or rogue employees.
Here are a few ways in which zero-trust security principles help secure the bots in financial process automation:
Using strong authentication and authorization mechanisms for the bots. The automation platform must verify the identity and permissions of the bots before allowing them to access any resource or data. The platform must identify a bot executing tasks for a customer organization from other bots executing tasks for different customer organizations. This is very critical in case of Multi-Tenant SaaS based models.
Implement least-privilege principle for your bots. This means that the bots are granted only the minimum level of access and permissions that they need to perform their tasks, and nothing more. This way, the bots are prevented from accessing data that is beyond the permissible boundaries and also limit the potential damage that a compromised or misused bot can cause.
Track and audit various activities of the bots. It is very critical to log and continuously monitor all the actions and transactions that the bots perform, such as what data they access, modify or delete, what systems they interact with, what errors or exceptions they encounter and so on. These logs need to be reviewed regularly using analytics tools to identify anomalies and suspicious patterns that may indicate a security breach or a compliance violation.
Organizations that look to optimize their financial processes through AI-driven SaaS automation solutions should evaluate the solutions paying special attention to the security aspects governing bots, and on how their organization’s data and critical digital assets are secured using security principles such as zero-trust.
In the rapidly advancing landscape of finance, the integration of Artificial Intelligence (AI) has ushered in unprecedented efficiencies and insights. As Chief Financial Officers (CFOs), your role not only involves steering financial strategy but also safeguarding the invaluable asset that is financial data. In the age of AI, where data is both currency and vulnerability, understanding and implementing robust security measures is paramount. This blog serves as an outline to fortifying financial data against the evolving challenges of the AI era.
The marriage of finance and AI has brought about transformative changes, streamlining processes, and enhancing decision-making capabilities. However, the reliance on AI also necessitates a comprehensive approach to data security ensuring privacy of the accounting and financial assets of an enterprise. Here are key strategies for CFOs and their teams to safeguard financial data in the age of AI:
One cannot overemphasize the importance of encryption in securing financial data. Implementing end-to-end encryption ensures that sensitive information remains indecipherable both in transit and at rest. Explore advanced encryption methods, such as homomorphic encryption, to enable secure processing without compromising data confidentiality. This directly maps to the regulatory compliances available to vet and test software and SaaS-based offerings in this space.
Robust access controls are pivotal in preventing unauthorized access to financial data. Utilize Role-Based Access Control (RBAC) to align data access privileges with job roles. This not only minimizes the risk of internal threats but also ensures that employees access only the data essential for their responsibilities.
Embrace AI-driven continuous monitoring to detect anomalies in real-time. Behavioral analytics, powered by AI algorithms, establish normal user patterns and promptly flag any deviations. Early detection is key to mitigating potential security threats before they escalate. Prefer tools that provide dashboards, alerts, and logging mechanisms to allow deep observability of the functionalities.
In an era where AI models often operate as black boxes, prioritize solutions and products that offer explainability and transparency towards product capabilities as well as a clear reason and interpretability of any processed output that may be visible. Understanding how AI algorithms reach decisions fosters trust, and accountability, and aligns with regulatory requirements. Ensure that the financial insights derived from AI are not only accurate but also comprehensible.
Tokenization-based approaches emerge as a powerful strategy when sharing financial data externally. By replacing sensitive information with tokens, even if intercepted, the data remains meaningless without the corresponding tokenization key. These strategies include Masking and Anonymization tools, Redaction policies and only sharing the data post-removal of this information. Additionally, deploy secure APIs for data exchange, ensuring the integrity and confidentiality of financial information.
Invest in comprehensive cybersecurity training programs for your finance team. Educate them on AI-specific cybersecurity risks and instill a culture of awareness. A well-informed team is your first line of defense against evolving cyber threats.
Develop and regularly update an incident response plan tailored to AI-related security incidents. Ensure that your team is equipped with clear procedures for identifying, containing, eradicating, recovering, and learning from security events. Preparedness is your best defense against unforeseen challenges.
As CFOs navigating the dynamic landscape of finance, embracing the power of AI comes with a concurrent responsibility to safeguard the integrity and confidentiality of financial data. By implementing robust encryption, enforcing stringent access controls, leveraging AI for continuous monitoring, and fostering a culture of cybersecurity awareness, you are not only fortifying your organization against evolving threats but also positioning it at the forefront of the AI-driven future.
At Hyprbots, we understand the paramount importance of data security in the financial realm. Our cutting-edge solutions not only harness the power of AI for financial optimization but also prioritize the highest standards of data protection. Together, letÂ’s navigate the future with confidence, ensuring that the transformative potential of AI in finance is realized securely and responsibly.
Securing Finance Data blog Series: This blog is an introductory piece towards blogs around finance data security. We will publish a weekly blog detailing various technical as well as user aspects on this topic.