Introducing Transactional Data Based GRC

Introducing Transactional Data Based GRC Introduction We are well into the 4th Industrial Revolution, with many companies now significantly invested in some kind of automation, Machine Learning or other AI related business improvement or optimization projects. There is no stopping the advancement of the 4IR future, but we can, as the human being, continue to ensure that we remain the architects of the journey into that future. As AI becomes more of a mainstream, business norm, we must ensure that we continue to consider the consequential impacts that AI automation brings. The positives are boundless, but the impact potential on both the existing human workforce and the ethical questions remain. This Blog seeks to take a brief look at some of the fundamentals, starting with a summary unpack of the EU AI Act, through to introduction of the concept of Transactional Data Based GRC. With the introduction of, the now passed EU Artificial Intelligence Act, which was passed in Mach this year, and expected to become official law by May or June this year, the EU, once again, takes a leadership role, as with GDPR, in providing guiding principles, this time, related to ethics and transparency in the use of AI as a business norm. Key provisions of the EU AI Act, include: Risk-Based Approach – The Act, categorizes AI systems into four risk categories based on their potential impact on safety, fundamental rights, and societal values. Higher-risk AI systems, such as those used in critical infrastructure, law enforcement, or healthcare, would be subject to stricter requirements and oversight. Prohibited Practices: The Act, sets to, prohibit, certain AI practices that pose unacceptable risks, such as AI systems that manipulate individuals through subliminal techniques or exploit vulnerable groups. Transparency and Traceability: The Act address, developers and providers of AI systems, who are required to ensure transparency and provide information about the AI’s capabilities, limitations, and potential risks. This includes documenting the data used to train AI models and providing explanations of AI-generated decisions where applicable. Data Governance: The Act emphasizes the importance of data governance and requires that AI systems be trained on high-quality data that is representative and free from bias. Additionally, data used for training AI models must comply with EU data protection rules. Human Oversight: Certain high-risk AI systems must undergo human oversight, including the ability for human intervention, monitoring, and verification. This is intended to ensure accountability and mitigate the risks associated with fully autonomous AI systems. Compliance and Enforcement: The proposed Act, outlines mechanisms for ensuring compliance with the regulations, including conformity assessments, certification schemes, and market surveillance. National competent authorities would be responsible for enforcement within their respective jurisdictions. International Cooperation: The EU AI Act emphasizes the importance of international cooperation on AI regulation and standards. It aims to promote alignment with international norms and facilitate cross-border cooperation on AI governance.   Overall, the EU AI Act represents a significant step toward regulating AI technology in the EU, with the goal of fostering trust, innovation, and responsible AI development and deployment, the world over. We have no doubt that, as with the GDPR Regulation, we will see rapid developments of similar and supporting local jurisdiction legislations and regulations, and ISO standards to come, in support of AI governance within the context of each country. As GRC professionals, it is vital that we continue to address the growing concerns, with more and more business as usual use of AI, which requires careful consideration of the ethical, social, and economic implications of AI in the workplace, as well as proactive efforts to mitigate potential negative consequences and ensure that the benefits of AI are equitably distributed among all workers, as we seek the value add that AI projects can bring to the organization. Introducing the Concept of Transactional Data Based GRC So, what is transactional data based GRC? As with all business functional areas, Governance, Risk and Compliance Management, including internal Audit, must evolve if it is to stay relevant to the organization in the age of AI and automation. The concept of Transactional Data Based GRC, incorporates an ISO Standards Model approach, in first defining the context of an organization, and then determination of all inputs, that will provide evidence to an expected output to the following aspects of GRC activities: Risk detection and identification Risk evaluation Underlying causal assessment Incident detection and reporting Near miss and emerging risk detection Determination of effectiveness or lack thereof, of controls Determination of correctness of policies and procedures, in control embedment Gaps leading to new risks and lost opportunities Control Assurance evidence Internal Audit continuous control monitoring Continuous Control Monitoring in lines of defense So how can AI be used effectively within the activities of governance, risk, compliance control management day to day activities? Using AI algorithms to analyze, source level, transactional data, whether that data be structured, (e.g., databases, spreadsheets) or unstructured (e.g., text documents, emails, WhatsApp messages, images, video), can be highly effective in automated continuous GRC management, in identifying evidence of, success or failure, of GRC activities in practice, within an organization on a real time or near real time basis. Here’s how GRC AI algorithms, run at source transactional data level, on structured or unstructured data, can be utilized for this purpose:   Hidden or Missed Risk Detection and Risk Discovery. The old ways of asking Risk Owners to endlessly keep system or more likely, an excel spreadsheet risk register up to date, are a thing of the past. o Risks will be identified at the transactional data level, which can be sourced from data both internal to the business, in systems and unstructured data, as well as external, in RSS feeds, open source, social media and through tools like Chat GPT, open AI, dark web sources etc. The role of the human then becomes to ensure the validity of the source data and validate the identified risks through again, a governance AI driven process. Data Quality then becomes the primary focus.   Emerging Risk Recognition

Unlocking Potential: The Benefits of Tailored Microsoft Licensing Solutions

Unlocking Potential: The Benefits of Tailored Microsoft Licensing Solutions Introduction In today’s dynamic business environment, optimizing IT resources and costs is crucial for maintaining a competitive edge. One of the most effective ways to achieve this is through tailored Microsoft Licensing solutions. These customized licensing strategies offer numerous advantages that go beyond standard offerings. Here’s a look at how tailored Microsoft Licensing can benefit organizations: 1. Cost Efficiency Tailored Microsoft Licensing solutions are designed to align with an organization’s specific needs, helping to ensure that you’re only paying for what you use. By customizing your licensing, you can avoid over-purchasing or under-utilizing software licenses, leading to significant cost savings. This approach allows for better budget management and more accurate financial forecasting. 2. Scalability Businesses grow and evolve, and so do their software needs. Tailored Microsoft Licensing solutions offer flexibility and scalability, making it easier to adjust licenses as your organization’s requirements change. Whether you’re expanding your workforce or adding new services, customized licensing can accommodate these changes without unnecessary delays or additional costs. 3. Enhanced Compliance Compliance with software licensing agreements is essential to avoid legal and financial penalties. Tailored licensing solutions help ensure that your organization remains compliant by providing clear insights into your licensing usage and requirements. This reduces the risk of non-compliance and helps streamline audits and reporting processes. 4. Optimized Resource Management Customized licensing helps in better management of software resources by aligning licenses with actual usage. This prevents issues such as underutilization of expensive software or the need for additional purchases. By understanding and managing your software needs more effectively, you can maximize the value of your investments. 5. Improved User Experience Tailored solutions ensure that users have access to the specific tools and features they need, enhancing productivity and efficiency. By matching the licensing to the precise needs of different teams or departments, you can avoid unnecessary features that might clutter the user experience or complicate workflows. 6. Streamlined Procurement Process A tailored approach simplifies the procurement process by providing a clear roadmap for acquiring and managing licenses. This can reduce administrative overhead and speed up the acquisition process, allowing your IT team to focus on more strategic initiatives rather than managing licensing details. 7. Strategic Planning and Support With tailored licensing solutions, organizations often receive enhanced support and strategic planning from Microsoft or their licensing partners. This includes personalized advice on optimizing software usage, future-proofing your IT investments, and navigating any complex licensing scenarios. 8. Future-Readiness Tailored licensing solutions are designed with future growth in mind. They help prepare your organization for technological advancements and shifts in software usage, ensuring that your licensing strategy evolves alongside your business. This proactive approach helps you stay ahead of potential challenges and seize new opportunities. Conclusion Tailored Microsoft Licensing solutions provide a strategic advantage by offering cost efficiency, scalability, compliance, and optimized resource management. By customizing your licensing approach to fit your organization’s specific needs, you can enhance productivity, streamline processes, and better manage your IT investments. In a world where technology is constantly evolving, a tailored licensing strategy ensures that your organization remains agile and prepared for the future.

The Alarming Rise of Cybercrime as a Service in 2024:A New Threat to Businesses

The Alarming Rise of Cybercrime as a Service in 2024: A New Threat to Businesses Introduction In 2024, the cybersecurity landscape has witnessed a troubling surge in the availability and sophistication of Cybercrime-as-a-Service (CaaS). This evolving trend allows even those with minimal technical expertise to launch devastating cyberattacks. This commodification of cybercrime poses severe threats to businesses, as it drastically lowers the barrier for executing attacks, leading to an increase in frequency and complexity. What is Crybercrime-as-a- Service? CaaS refers to the commercialization of cyberattack tools and services, which are now readily available on the dark web. These services include everything from ransomware and phishing kits to Distributed Denial of Service (DDoS) attacks and hacking-for-hire schemes. Cybercriminals offer these services at various price points, enabling anyone to become an attacker with minimal effort or knowledge. In 2024, the rise of CaaS has led to a significant increase in cyber incidents. One notable example is the LockBit ransomware gang, which has been particularly active this year. In May 2024, LockBit targeted the healthcare sector, including a cyberattack on the Wichita government’s systems, forcing them to shut down critical infrastructure. The impact was severe, with disruptions to essential services like payments and airport operations. Similarly, the LockBit group attacked the healthcare system of Singing River Health System, resulting in the theft and leak of data from nearly 900,000 individuals. Implications for Businesses The rise of CaaS is a wake-up call for businesses worldwide. The commoditization of cybercrime means that traditional security measures may no longer suffice. Companies need to enhance their cybersecurity posture by adopting a multi-layered defense strategy, which includes: Advanced Threat Detection: Investing in next-generation firewalls, intrusion detection systems, and continuous monitoring to identify and respond to threats in real-time. Employee Training: Since many CaaS attacks, such as phishing, exploit human vulnerabilities, regular training programs are crucial to raise awareness and prevent social engineering attacks. Incident Response Planning: Given the inevitability of some attacks, businesses must have robust incident response plans in place to minimize the damage and recover swiftly. Collaboration with Law Enforcement: Companies should work closely with cybersecurity experts and law enforcement to stay informed about the latest threats and respond effectively when attacked. Conclusion The rise of CaaS has made cybersecurity a critical boardroom issue. Businesses can no longer afford to be reactive; they must be proactive in their defense strategies, investing in the necessary tools, technologies, and training to safeguard their assets against this growing threat. By staying vigilant and prepared, organizations can mitigate the risks posed by this alarming trend and ensure that they are not the next victims of an increasingly accessible and dangerous cybercrime ecosystem.