5-step AI Framework to Acing Cyber Security Regulatory Compliance

Nov 30, 2021

Banking | 10 min READ

Financial institutions (FIs) are adopting revolutionary new technology solutions to complement their existing compliance functions in a rapidly changing regulatory environment. Tools such as artificial intelligence (AI), machine learning (ML), natural language processing (NLP), blockchain, and intelligent tagging are becoming increasingly important in refining compliance productivity allowing compliance professionals to focus on other operational cyber resilience matters.

Sanjay H. Chaswall

Banking & Capital Markets Growth Leader

Life Sciences

Birlasoft

Mark Weston

Investor & Founder

Regulativ.ai

Artificial Intelligence and Cybersecurity

The financial industry spends over $181 billion maintaining compliance every year. According to a recent survey by the Risk Management Association, 50% of the firms surveyed spend 6-10% of their revenue on compliance costs. Large firms state that the average cost of maintaining compliance is approximately $10,000 per employee. The cost of industry regulation amounts to an 8% tax on financial firms. The fast-changing regulatory compliances landscape results in financial firms often failing to comply with the required standards simply because they are still reliant on repetitive manual tasks.

Without automation, reputational and brand damage will occur, and the entire exercise can be expensive and labor-intensive. Financial firms are fast turning to AI and big data as part of their regulatory technology strategy to remain compliant in today's data-centric world. The technologies enable financial firms to capitalize on the potential and value of next-gen regulatory compliance. Through AI, they can interpret regulatory compliance documents faster. Furthermore, AI-assisted applications offer actionable insights after consolidating lengthy and complex compliance documentation into shorter and easily readable text, therefore driving maximum value from the data.

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Cyber Security Regulatory Compliance

Cyber attackers always seem to be one step ahead of security experts so we can expect the current cybersecurity landscape to become more rigorous building on regulations like the recent GDPR and California's Consumer Privacy Act. Many European companies are juggling between the constraints set by the implementation of GDPR, and at the same time, complying with the minimum industry standards to avoid being fined. The biggest obstacle to fulfilling regulatory requirements is that they become outdated quickly in the face of rapidly evolving technologies in cyberspace, constantly putting a strain on entire compliance ecosystem.

Governing bodies are continually changing the regulatory goalposts which adds significant demand to already overstretched cybersecurity teams. CISOs are struggling to comply with fast-changing external requirements at the same time as safeguarding their customers' data today. This increasing burden points towards the quick adoption of newly available technology tools that not only aid CISOs in making the entire regulatory compliance process seamless but also prove to be forward-looking when it comes to encapsulating and alerting the compliance teams on the changing regulations to avoid hefty fines and keeping the financial firms compliant at all times.

Importance in the Financial Services World

Recent studies by the Boston Consulting Group highlight the growth of cybersecurity regulatory requirements. According to the report, more than 30 cybers regulations have been announced in the United States alone in the last seven years. While these regulations strive to establish a set of robust cybersecurity practices to protect consumers and support the global economy's stability, they use different vocabularies and lexicons to communicate the same concepts and techniques, exerting a significant burden on the financial services industry. Smaller financial services companies work with 2 to 3 regulators, while larger firms employ 10, 20, or even more regulators to ensure compliance. Such a complex regulatory environment only results in inefficiencies, lost time, and substantial financial impacts for financial institutions.

According to the Banking Policy Institute, one chief information security officer indicated that he and his team spent nearly 40% of their work time reconciling various cybersecurity and regulatory frameworks. At another multinational bank, the CIO, head of the audit, and dozens of operating personnel had to conduct a two-month analysis of the bank's cybersecurity compliance, consuming 15% of the operating budget for the bank's technology risk and compliance function for the entire year.

By using AI and big data, financial firms can make their regulatory compliance process smart. These tools enable businesses to understand and predict intricate patterns in risk data. Additionally, banks can improve their compliance process by adopting cloud architectures, which help them securely store data. With benefits far outweighing the associated risks, AI is fast being adopted in the cybersecurity compliance landscape.

Putting AI to Work: Cyber Security Regulatory Compliance Framework

There are many established cyber security compliance frameworks that can help an organization tie its processes to established industry requirements, specifications, and government legislation. CISOs and other cyber security professionals must determine their organizations' specific needs to match them to the appropriate framework. Governance, risk management, and compliance frameworks established by NIST, PCI DSS, ISO, ISACA, GLBA, and the FFIEC strive to assess risk and identify security gaps. While they offer valuable recommendations for cyber risk management, applying and perfecting a cybersecurity strategy can overwhelm capable but short-handed IT security staff; this is where deploying AI makes perfect business sense.

5-step AI Framework to Acing Cyber Security Regulatory Compliance

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#1 Analyzing and abstracting regulatory policies issued globally and locally

A Thomson Reuters survey of compliance professionals highlights that regulatory policies are getting updated more than 200 per day on an average, approximately one change every seven minutes. Regulatory standards also change overnight, sometimes due to various market forces. An inability to adhere to the new regulations may risk financial companies' brand equity and even result in significant financial penalties. Cyber regulatory compliance and reporting applications help such companies remain compliant by notifying them regularly about regulatory changes and making the necessary updates automatically for them. These applications use NLP and deep learning to read compliance requirements. The platform takes care of the need to keep up with regulatory change for the financial firms that use it.

AI/ML solutions assist financial institutions in automatically identifying, analyzing, interpreting, and even implementing the new/revised regulatory mandates to an extent. Fintechs can leverage NLP and cognitive computing capabilities to proactively scan through, evaluate and interpret vast volumes of unstructured regulatory content dispersed across various websites and databases of regulators.

The solution automatically shortlists applicable regulatory requirements for the FI. An AI-powered model can extract metadata and map the new/changed requirements to the financial institutions' products, services, contracts, processes, and functions. The system then translates these requirements into common machine-executable form and links these to the related policies, procedures, and systems of the affected business/compliance functions of fintech.

Compliance processes and reporting procedures are automatically kept up to date with regulatory changes on an event driven basis. As the regulation changes, the platform updates all clients with the new reporting output. If new data is required to satisfy the requirement, then this is gathered first manually for speed, and secondly via automation data integration.

Service providers and regulators can then implement this technology to interface with massive volumes of data brought about by real-time system updates and systemic changes, using grouping, intelligent tagging, and deduplication to analyze the results. This allows higher productive time for employees as utility staff can focus on the core business operations that require attention. By developing semantic technology and data point models that convert regulatory text into NLP systems, FIs can integrate AI and ML into their existing framework and update documents & processes with every regulatory text update.

#2 Aggregation of data sourced by the entity from API or manual efforts

Every reporting process FIs engage in involves many documents and typically repetitive manual processes. NLP and Robotic Process Automation (RPA) are particularly useful in meeting compliance requirements. Meanwhile, scenario comparisons are essential for Comprehensive Capital Analysis and Review (CCAR), Dodd-Frank, and European Market Infrastructure Regulation (EMIR) stress tests. FIs have to integrate data from thousands of regulatory publications each month to deal with regulatory change management throughout the organization. These changes involve complex interactions between different business areas and have spiraling effects on other processes as well. FIs might need to restructure a portfolio based on regulations, which can impact each asset within the portfolio and potentially other portfolios.

To achieve rapid ‘time to value’ with a platform, the recommended approach is to use a 2-stage approach. Stage 1 involves identifying the source of each data attribute and rapid-sourcing using requests for information via email and secure web forms. This data can be evidenced and validated using a ‘maker checker’ approach. Stage 2, which can be delivered in parallel with stage 1 to some extent, is to identify opportunities to integrate with existing systems, prioritize and deliver the integrations. Stage 1 delivers 30% of the benefit by obtaining the data from the organization once in a logical and structured format. This then unlocks the potential to service all required regulations and other cyber control frameworks such as NIST, ISO27001 and SOC2. Stage 2 will deliver 40% or more in further efficiencies, depending on the Opportunity and appetite to deliver integration.

Additionally, AI can bring together all data sources, do advanced searches, and perform complete investigations more efficiently. Through integrating AI and NLP, CISOs can augment their cybersecurity compliance measures by effectively analyzing data coming out of a security tech stack. This ultimately helps them understand how various tools and solutions achieve cybersecurity regulatory compliance programs across standards.

#3 Assessment of submissions made for compliance needs using AI and machine learning models

Large organizations have vast and fragmented compliance data sets. This presents a huge challenge for risk and compliance teams. Part of the challenge is identifying the correct source of data and the veracity of that data. At the output end of the process, the compliance team must assess the overall submission.

AI can help the team at this stage in performing comparative analytics between different regulatory submissions and assisting by compliance teams to quickly understand where changes to controls may need to be made. Artificial intelligence supports compliance officers in automating all elements of their communications data management, including data capturing, enriching it with third-party data, investigating seamlessly, archiving, and retaining data.

#4 Preparing responses to regulators using available and historical data

Regulatory compliance platforms include the latest in visual analytics and business intelligence software. In addition to preparation of the regulatory compliance output in the required format, the platform provides a wide range of key indicators around data, process, people and crucially evidence.

When combined with the latest blockchain technology, artificial intelligence allows firms to share their data safely across decentralized structures. Blockchain enables several actions to be documented and kept in one shared, secured, permanent location while eliminating the pressure associated with segregated record-keeping and saving money due to the speed and accuracy associated with the regulatory review process. Workflow automation, in this regard, enhances cybersecurity regulatory compliance oversight, coordination, and collaboration.

Automated workflows capture real-time communication between CISO's office, regulators, and tech teams, thereby effectively collaborating between multiple teams. This way the response mechanism becomes more efficient and sophisticated for CISOs.

#5 Final review of the assessment by CISO office before final submission to regulators

A human review is always recommended. AI is extremely capable, but nothing beats a human check and attestation to keep the regulator happy. AI possesses the intrinsic ability to extract maximum value from data, helps in decision-making, and eases compliance professionals' burdens. It automates repetitive tasks, which frees compliance professionals to focus on other areas that need attention.

With the evolution of AI in cybersecurity, FIs are better equipped to capture, analyze and filter various data elements. AI and RPA help companies reduce false positives and costs. It alleviates human error by emphasizing blind spots, reasonable errors, and other potential errors that a compliance professional might miss. Once AI-assisted tools complete all these processes, the last leg of the process – final review by CISOs becomes efficient, much faster, and most importantly, provides a much higher quality and assured output

Today compliance has transformed from being a value protector to a value creator. Ably backed by AI and other technology, FIs can prevent costly fines and audits and are gaining a competitive edge. According to Intertrust, 94% of UK-based financial services industry decision-makers believe that AI has the most significant potential to revolutionize the sector in the near future. With the proper AI strategy in place, CISOs can eliminate extraneous content, obtain alerts on the riskiest behavior and optimize their cybersecurity compliance framework.

For financial firms looking to enhance efficiency, reduce review time and quickly determine misconduct before any adverse eventuality, now is the time to integrate AI into the cybersecurity regulatory compliance approach and make their cybersecurity compliance strategy a fool-proof one.

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