This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Risk is inescapable. A PwC Global Risk Survey found that 75% of risk leaders claim that financial pressures limit their ability to invest in the advanced technology needed to assess and monitor risks. Yet failing to successfully address risk with an effective risk management program is courting disaster.
To counter such statistics, CIOs say they and their C-suite colleagues are devising more thoughtful strategies. Here are 10 questions CIOs, researchers, and advisers say are worth asking and answering about your organizations AI strategies. How does our AI strategy support our business objectives, and how do we measure its value?
Welcome to your company’s new AI risk management nightmare. Before you give up on your dreams of releasing an AI chatbot, remember: no risk, no reward. The core idea of risk management is that you don’t win by saying “no” to everything. Why not take the extra time to test for problems?
This year saw emerging risks posed by AI , disastrous outages like the CrowdStrike incident , and surmounting software supply chain frailties , as well as the risk of cyberattacks and quantum computing breaking todays most advanced encryption algorithms. To respond, CIOs are doubling down on organizational resilience.
They rely on data to power products, business insights, and marketing strategy. From search engines to navigation systems, data is used to fuel products, manage risk, inform business strategy, create competitive analysis reports, provide direct marketing services, and much more.
Third, any commitment to a disruptive technology (including data-intensive and AI implementations) must start with a business strategy. I suggest that the simplest business strategy starts with answering three basic questions: What? Test early and often. Test and refine the chatbot. Expect continuous improvement.
The proof of concept (POC) has become a key facet of CIOs AI strategies, providing a low-stakes way to test AI use cases without full commitment. Companies pilot-to-production rates can vary based on how each enterprise calculates ROI especially if they have differing risk appetites around AI. Its going to vary dramatically.
Weve seen this across dozens of companies, and the teams that break out of this trap all adopt some version of Evaluation-Driven Development (EDD), where testing, monitoring, and evaluation drive every decision from the start. What breaks your app in production isnt always what you tested for in dev! The way out?
Despite AI’s potential to transform businesses, many senior technology leaders find themselves wrestling with unpredictable expenses, uneven productivity gains, and growing risks as AI adoption scales, Gartner said. CIOs should create proofs of concept that test how costs will scale, not just how the technology works.”
Regardless of the driver of transformation, your companys culture, leadership, and operating practices must continuously improve to meet the demands of a globally competitive, faster-paced, and technology-enabled world with increasing security and other operational risks.
CIOs perennially deal with technical debts risks, costs, and complexities. While the impacts of legacy systems can be quantified, technical debt is also often embedded in subtler ways across the IT ecosystem, making it hard to account for the full list of issues and risks.
By articulating fitness functions automated tests tied to specific quality attributes like reliability, security or performance teams can visualize and measure system qualities that align with business goals. Technical foundation Conversation starter : Are we maintaining reliable roads and utilities, or are we risking gridlock?
In our previous post Backtesting index rebalancing arbitrage with Amazon EMR and Apache Iceberg , we showed how to use Apache Iceberg in the context of strategy backtesting. This capability is particularly valuable in maintaining the integrity of backtests and the reliability of trading strategies.
Financial institutions have an unprecedented opportunity to leverage AI/GenAI to expand services, drive massive productivity gains, mitigate risks, and reduce costs. GenAI is also helping to improve risk assessment via predictive analytics.
One of them is Katherine Wetmur, CIO for cyber, data, risk, and resilience at Morgan Stanley. Wetmur says Morgan Stanley has been using modern data science, AI, and machine learning for years to analyze data and activity, pinpoint risks, and initiate mitigation, noting that teams at the firm have earned patents in this space.
million —and organizations are constantly at risk of cyber-attacks and malicious actors. In order to protect your business from these threats, it’s essential to understand what digital transformation entails and how you can safeguard your company from cyber risks. What is cyber risk?
Fragmented systems, inconsistent definitions, legacy infrastructure and manual workarounds introduce critical risks. I aim to outline pragmatic strategies to elevate data quality into an enterprise-wide capability. This challenge remains deceptively overlooked despite its profound impact on strategy and execution.
GRC certifications validate the skills, knowledge, and abilities IT professionals have to manage governance, risk, and compliance (GRC) in the enterprise. Enter the need for competent governance, risk and compliance (GRC) professionals. What are GRC certifications? Why are GRC certifications important?
Let’s get started with a comprehensive cybersecurity strategy for your small business. The first step of a well-planned cybersecurity strategy is identifying the avenues of attack in your system. Before prioritizing your threats, risks, and remedies, determine the rules and regulations that your company is obliged to follow.
Prebuilt features and templates will have already been performance tested, and they typically come at much lower price points than developing a product from scratch. Automate Your Testing AI technology can also help create other AI applications. One of the benefits is that it can help with automating coding and testing.
As IT landscapes and software delivery processes evolve, the risk of inadvertently creating new vulnerabilities increases. These risks are particularly critical for financial services institutions, which are now under greater scrutiny with the Digital Operational Resilience Act ( DORA ).
1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. These recommendations are based on our experience, both as a data scientist and as a lawyer, focused on managing the risks of deploying ML. 6] Debugging may focus on a variety of failure modes (i.e., Sensitivity analysis.
Adding smarter AI also adds risk, of course. “At The big risk is you take the humans out of the loop when you let these into the wild.” When it comes to security, though, agentic AI is a double-edged sword with too many risks to count, he says. “We That means the projects are evaluated for the amount of risk they involve.
While tech debt refers to shortcuts taken in implementation that need to be addressed later, digital addiction results in the accumulation of poorly vetted, misused, or unnecessary technologies that generate costs and risks. million machines worldwide, serves as a stark reminder of these risks.
Jayesh Chaurasia, analyst, and Sudha Maheshwari, VP and research director, wrote in a blog post that businesses were drawn to AI implementations via the allure of quick wins and immediate ROI, but that led many to overlook the need for a comprehensive, long-term business strategy and effective data management practices.
Although some continue to leap without looking into cloud deals, the value of developing a comprehensive cloud strategy has become evident. Without a clear cloud strategy and broad leadership support, even value-adding cloud investments may be at risk. There are other risks, too. Why are we really going to cloud?
Business risk (liabilities): “Our legacy systems increase our cybersecurity exposure by 40%.” Suboptimal integration strategies are partly to blame, and on top of this, companies often don’t have security architecture that can handle both people and AI agents working on IT systems. Also, beware the proof-of-concept trap.
Rather than wait for a storm to hit, IT professionals map out options and build strategies to ensure business continuity. This may involve embracing redundancies or testing new tools for future operations. The disruption from VMware’s acquisition has led many to reconsider their virtualization strategies and explore new options. “By
Today’s cloud strategies revolve around two distinct poles: the “lift and shift” approach, in which applications and associated data are moved to the cloud without being redesigned; and the “cloud-first” approach, in which applications are developed or redesigned specifically for the cloud.
Unexpected outcomes, security, safety, fairness and bias, and privacy are the biggest risks for which adopters are testing. We’re not encouraging skepticism or fear, but companies should start AI products with a clear understanding of the risks, especially those risks that are specific to AI.
As CIO, you’re in the risk business. Or rather, every part of your responsibilities entails risk, whether you’re paying attention to it or not. There are, for example, those in leadership roles who, while promoting the value of risk-taking, also insist on “holding people accountable.” You can’t lose.
One of the best ways that cybersecurity professionals are leveraging AI is by utilizing SAST strategies. A big part of what enables this constant deployment of new applications is a testing process known as static application security testing, or SAST. It is frequently referred to as “white box testing.”
However, this perception of resilience must be backed up by robust, testedstrategies that can withstand real-world threats. One major gap in the findings is that four in ten respondents admitted their organization had not reviewed its cyber resilience strategy in the last six months.
Not instant perfection The NIPRGPT experiment is an opportunity to conduct real-world testing, measuring generative AI’s computational efficiency, resource utilization, and security compliance to understand its practical applications. For now, AFRL is experimenting with self-hosted open-source LLMs in a controlled environment.
These changes can expose businesses to risks and vulnerabilities such as security breaches, data privacy issues and harm to the companys reputation. It also includes managing the risks, quality and accountability of AI systems and their outcomes. AI governance is critical and should never be just a regulatory requirement.
In fact, successful recovery from cyberattacks and other disasters hinges on an approach that integrates business impact assessments (BIA), business continuity planning (BCP), and disaster recovery planning (DRP) including rigorous testing. See also: How resilient CIOs future-proof to mitigate risks.)
When it comes to implementing and managing a successful BI strategy we have always proclaimed: start small, use the right BI tools , and involve your team. Your Chance: Want to test an agile business intelligence solution? You need to determine if you are going with an on-premise or cloud-hosted strategy.
What are the associated risks and costs, including operational, reputational, and competitive? Find a change champion and get business users involved from the beginning to build, pilot, test, and evaluate models. Consultants can help you develop and execute a genAI strategy that will fuel your success into 2025 and beyond.
For those rare enterprises where innovation is more than a bullet point on a strategy statement embedded keep inside their SEC 10K, there is a repeatable approach for addressing the emerging unknown with great certainty. You risk adding to the hype where there will be no observable value. Test the customer waters.
Organizations are under pressure to demonstrate commitment to an actionable sustainability strategy to meet regulatory obligations and to build positive market sentiment. We examine the opportunity to lead both risk mitigation and value creation by helping advance the enterprise sustainability strategy.
And they need people who can manage the emerging risks and compliance requirements associated with AI. Staffing strategies emerge Despite the continuously tight labor market and complexity of the task, Napoli believes he has Guardian Life’s AI talent strategy under control. Here’s how IT leaders are coping.
Synthetic data can also be a vital tool for enterprise AI efforts when available data doesn’t meet business needs or could create privacy issues if used to train machine learning models, test software, or the like. Artificial data has many uses in enterprise AI strategies. Synthetic data use cases. Synthetic data generation.
CIOs are now reassessing the strategies to transform their organizations with gen AI, but its not exactly time to throw out the work thats already been done. That echoes a statement issued by NVIDIA on Monday: DeepSeek is a perfect example of test time scaling.
particular, companies that use AI systems can share their voluntary commitments to transparency and risk control. At least half of the current AI Pact signatories (numbering more than 130) have made additional commitments, such as risk mitigation, human oversight and transparency in generative AI content.
We organize all of the trending information in your field so you don't have to. Join 42,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content