Technology has become deeply integrated into nearly every aspect of business operations over the past few decades. Companies rely on advanced IT systems and software from cloud computing to artificial intelligence to improve efficiency, cut costs, and gain strategic advantages.
However, greater dependence on technology also introduces new cybersecurity, privacy, and operational risks that must be carefully assessed and managed. By the beginning of 2025 and the end of 2024, the burden and cost of cyber attacks on the global economy are predicted to be above 10 trillion dollars.
This is where third-party risk assessment and technology risk management come into play. Here in this blog, we will delve into the pain points and how automating the risk assessment is the way forward.
Technology As a Double-Edged Sword
As businesses adopt more technologies, they often have third parties, like software vendors or cloud providers, host, manage or have access to their IT environments and sensitive data which mandates the need to manage third-party risks in technology. While this can enable innovation and scalability, it also creates major risks if these vendors have poor security controls or unreliable services. Some high-profile third-party breaches, like the SolarWinds and Kaseya attacks, have highlighted the dangers here. That’s why rigorous third-party risk assessments are essential – they evaluate vendors’ security, resilience, and compliance to determine potential weaknesses that could disrupt the business. Regular technology risk management serve a similar purpose in evaluating a company’s internal environments and controls.
The Challenges of Manual Risk Assessments
Historically, third-party and technology risk assessments have been very manual processes. Supply chain, infosec, and technology teams would send lengthy questionnaires to vendors to self-assess their security, availability, and compliance controls. The questionnaires often contained probing questions in order to reveal any leakages and pain points in the systems and procedures employed by the third parties. Teams would then painstakingly review hundreds of pages of documents and try to validate the responses through interviews and audits. However, this manual approach has some major limitations:
- It is incredibly labor and time-intensive for both vendors and assessors. Completing detailed questionnaires can take weeks for vendors, while reviewers require months of work parsing responses. This frustrates vendors and strains internal teams.
- Self-reported data from vendors is hard to validate. Without independent verification, it can be difficult for assessors to feel confident in the accuracy of submissions.
- Dynamic environments make it hard to keep assessments current. With vendors updating systems frequently and new vulnerabilities emerging, assessments become outdated quickly. But refresh cycles are typically only once a year.
- Benchmarking and visibility across the vendor portfolio is limited. With decentralized and inconsistent evaluations, it’s hard to compare risk levels across different third parties or technologies.
Automating Assessments with Continuous Monitoring
Thankfully, there are emerging solutions to help automate, centralize, and continuously refresh technology risk assessments. New automated tools can regularly capture security configurations, vulnerability data, compliance controls, and other key risk indicators from an organization’s entire IT environment, including third-party connected systems. Dashboards can then calculate risk scores across categories like security, resilience, or compliance while benchmarking vendors. exception-based alerts also notify teams proactively when risks emerge, enabling rapid response. Here’s how tech automation impacts risk assessment
More Comprehensive Assessments
Automated systems have the capacity to digest and cross-reference vast volumes of technical documentation, user feedback, incident reports, and usage data to uncover risks that a manual assessment approach may miss. By leveraging the computational power of machines, risk assessments can be more data-driven, rigorous, and comprehensive. Technologies such as natural language processing enable analyzing qualitative data like support tickets, user reviews, or social media posts to surface human-reported issues. Automation also facilitates continuously monitoring and re-evaluating risks throughout the technology lifecycle.
Conducting tech risk evaluations quickly enough to inform key decisions has become a pressing need. The accelerated pace of development and frequent updates to software, devices, and platforms means risks can emerge just as rapidly. Automation speeds up data collection, analysis, and reporting processes to provide decision-makers timely insight into technology risks and risk management tradeoffs to guide development or deployment actions.
More Consistent and Unbiased Analysis
Leveraging predefined assessment models and algorithms helps minimize human subjectivity and cognitive biases that can lead to inconsistent or incomplete evaluations. Automated systems apply the same criteria and logic systematically across all technologies under review. This supports more equitable risk analysis, more standardized benchmarking between products or vendors, and easier auditing.
While automation enhances the efficiency, scale, and consistency of tech risk assessments, AI algorithms and models are narrow in their capabilities. Human oversight and judgment play a critical role in setting risk evaluation goals, defining parameters, interpreting results, validating predictions, and making final calls on risk levels or required actions. People also bring important context about business needs, user expectations, and ethical implications. The most robust approach is augmented intelligence where human strengths complement automated processing.
Risk of Over-Reliance on Algorithms
As powerful as they are, AI systems used in risk assessment and third-party risk management have limitations around explainability and transparency. Their inner logic and decision-making can be black boxes. Over-reliance on algorithm outputs without enough human verification can allow critical flaws or biases to be overlooked. There are also risks of perpetuating historical biases if training data itself reflects an uneven impact on different demographic groups that algorithms learn to embed. Responsible development and deployment of automation in tech risk assessment include thoughtful design, extensive testing, and ongoing monitoring for fairness and safety.
With strong technology risk management fueled by automation, companies can harness IT capabilities to drive their business forward in a responsible way even in today’s complex, dynamic environment full of emerging cyber threats. Those organizations that leverage these solutions will have a clear competitive advantage in their industries going forward.
At Beaconer, we provide managed third-party risk solutions, tech risk assessment and management, and comprehensive third-party risk management. Powered by our cloud-native AI platform and advanced analytics, our solutions take charge of vendor risks that threaten your business.