As industries try to play catch-up as COVID-19 pushed everyone to remote work, technical debt is more of an issue than ever before. Technical debt is a term that describes the implied cost of additional work, or re-work, caused by choosing an easy solution over a better solution that may take longer to implement. Like financial debt, if technical debt isn’t dealt with or “repaid,” it can accumulate “interest,” making it more difficult for companies to implement sweeping changes that could improve the organization or improve the software.
As artificial intelligence (AI) and automation become more pervasive in the modern world, one of the areas they focus on is this technical debt that organizations have mired themselves. Without AI solutions, the debt will build and build, making software more bloated and more modular the longer it is in effect. The solution lies in automation where the technical debt can be addressed and further improve an organization's cyber resiliency and where return on security investment (RoSI) can be calculated.
Automation is especially appealing to those in cybersecurity when looking at GRC solutions that are modular and siloed and have difficulty “talking” to one another. This also contributes to technical debt and requires human intervention as it accumulates. For many organizations, execution, governance, and operational success are elusive because there have not been the same kind of breakthroughs for most cybersecurity practices compared to other industries that use AI, and the awareness of automation options remains low.
According to Gartner though that’s changing, 30% of enterprises plan to increase AI investments since the start of the pandemic, and 47% of AI investments remain unchanged. The ability for a company to automate also depends on their maturity level. Companies must have a sufficiently mature security program in order to take advantage of automation opportunities. If automation was implemented on a larger scale, security incidents, financial loss, data loss, and data breaches could be mitigated more effectively.
Going past regular automation into advanced, AI-fueled automation
Automation is slowly but surely transforming from an optional condition to a means of survival. Although there was a demand for it pre-pandemic, the global crisis has accelerated digital transformation initiatives and broken down some of the obstacles higher-level executives had with automation processes.
AI encompasses a vast range, including machine learning (ML), natural language processing (NLP), Robotic process automation (RPA), and more. NLP especially plays a significant part in advanced automation. NLP’s ultimate objective is to “read,” decipher, and understand language that’s valuable to the end-user. Currently, there are several ways NLP is used in day-to-day life. Many are familiar with chatbots or auto-complete in emails or texts. But there’s a gap in cybersecurity and risk assessment where NLP could be used to inform risk management and regulatory compliance. Since interactions between humans and machines are based on language processing, NLP allows organizations to process increasingly large amounts of data, granting them the ability to be more efficient, more risk cognizant, and more secure.
Incident detection and prediction is one area where humans can take advantage of AI. NLP used for risk and compliance requirements can identify overlaps in frameworks and data from an enterprise’s tech stack and use it to identify vulnerabilities in security infrastructure.
However, there are a number of AI-fueled cyber security solutions out there, and it has definitely become a buzzword in cyber. These GRC automation tools still tend to require human oversight and intervention. They do not achieve pure automation. In this industry, automation tends to encompass employees and security leaders getting texts or emails when controls need to be addressed or updated, but what if there were more powerful options that allowed for an automated system that could identify how threats endanger your current tech stack?
At an operational level, advanced automation and controls address risk and the plague of technical debt that business leaders and IT face. Automation allows executives to allocate resources in a way that will yield the greatest return on information security investment and gives them the opportunity to augment as many processes and complete as much systems integration as possible to increase resilience, efficiency, and agility. Doing away with siloed data and technical debt that cripples monitoring strategy allows security teams to increase their maturity and allocate resources to automation options that give a significant return on investment and to calculate the return.
How advanced automation can break the shackles of legacy systems
Advanced automation is critical in addressing modern security challenges by increasing speed and efficiency while cutting back on operational costs. Solving the pent-up data debt frees up vital resources and employees to focus on other matters and reduces inefficiency.
By combining AI and NLP, security leaders can make sense of data coming out of a security tech stack, showing where and how various tools and solutions manage compliance programs across standards. NLP allows for improvements over time with self-learning to become more efficient in enhancing cybersecurity processes. The automation of assessments gives business leaders insight into real-time risk monitoring.
“Crosswalking” is a process where the NLP engine identifies keywords that map to specific controls and control actions. Currently, the process of crosswalking in many cybersecurity solutions is manual and inexact. NLP gives organizations the ability to leverage nascent data that’s coming out of a platform. When other cybersecurity companies discuss crosswalking it’s typically behind a closed door, and no one knows how it happens or what it does. Mapping different frameworks don’t always provide a direct 1:1 solution. So having an option for automation that is transparent, thorough, and learns, is critical in increasing maturity and understanding.
CyberSaint takes a deep learning approach that allows crosswalking between frameworks without a significant amount of human intervention. This increases security maturity and makes the organization more risk cognizant. This becomes even more key when discussing cloud-based shifts post COVID-19.
Many Fortune 500 companies use spreadsheets to track and monitor risks and vulnerabilities, but this incredibly time-consuming task, typically done by an employee manually checking controls. By the time IT professionals reach the end of the spreadsheet, it can already be out of date. This leaves organizations vulnerable and consumes valuable resources that could be allocated elsewhere. In the era of COVID-19, with many companies downsizing their security budgets, this can be a massive blow to a cybersecurity program as resources become scarcer and employees gain an ever-growing list of responsibilities.
Conclusion
With automated risk platforms, it’s possible to manage risks and increase program maturity over time, dynamically. Software that can continuously prioritize threats and add more automation over time across compliance, risk, and audit can make a sizeable difference in downsized security teams. It becomes possible to track tangible, measured impacts and returns on investment with a balance between quantitative and qualitative data.
Executives need to start demanding more and expecting more of their IT GRC stacks. To achieve operational excellence, they need to shed the technical debt and move forward with automation to augment current processes and make them perform for them instead of constantly utilizing workarounds that are inefficient and leave critical systems open to risk.
To learn more about risk management and how automation is changing the landscape of cybersecurity, click here. To see a demonstration of CyberStrong and how it can help your enterprise to more with less, contact us.
