March 07, 2025
Kannan Subbiah
FCA | CISA | CGEIT | CCISO | GRC Consulting | Independent Director | Enterprise & Solution Architecture | Former Sr. VP & CTO of MF Utilities | BU Soft Tech | itTrident
The right tooling can make a company’s processes visible and accessible to more than just its process experts. With strategic stakeholders and lines of business users involved, the very people who best know the business can contribute to innovation, design new processes and cut out endless wasted hours briefing process experts. AI, essentially, lowers the barrier to entry so everyone can come into the conversation, from process experts to line-of-business users. This speeds up time-to-value in transformation. ... Rather than simply ‘survive,’ companies can use AI to build true resilience — or antifragility — in which they learn from system failures or cybersecurity breaches and operationalize that knowledge. By putting AI into the loop on process breaks and testing potential scenarios via a digital twin of the organization, non-process experts and stakeholders are empowered to mitigate risk before escalations.?... Non-process experts must be able to make data-driven decisions faster with AI powered insights that recommend best practices and design principles for dashboards. Any queries that arise should be answered by means of automatically generated visualizations which can be integrated directly into apps — saving time and effort.?
CISOs are asked to balance business objectives alongside product and infrastructure security, ransomware defense, supply chain security, AI governance, and compliance with increasingly complex regulations like the SEC's cyber-incident disclosure rules. Increased pressure for transparency puts CISOs in a tough situation when they must choose between disclosing an incident that could have adverse effects on the business or not disclosing it and risking personal financial ruin. ... The vCISO model emerged as a practical solution, particularly for midsize companies that need executive-level security expertise but can't justify a full-time CISO's compensation package.?... The surge in vCISOs should serve as a warning to boards and executives. If you're struggling to retain security leadership or considering a virtual CISO, you need to examine why. Is it about flexibility and cost, or have you created an environment where security leaders can't succeed? The pendulum will inevitably swing back as organizations realize that effective security leadership requires consistent, dedicated attention. ... Your CISO is working hard to protect your organization. So who will protect your CISO? Now is a great time to check in on them. Make sure they feel like they're fighting a winnable fight.?
An effective AI governance platform includes four fundamental components: data governance, technical controls, ethical guidelines and reporting mechanisms, says Beena Ammanath, executive director of the Global Deloitte AI Institute. "Data governance is necessary for ensuring that data within an organization is accurate, consistent, secure and used responsibly," she explains in an online interview. Technical controls are essential for tasks such as testing and validating GenAI models to ensure their performance and reliability, Ammanath says. "Ethical and responsible AI use guidelines are critical, covering aspects such as bias, fairness, and accountability to promote trust across the organization and with key stakeholders." ... "AI governance requires a multi-disciplinary or interdisciplinary approach and may involve non-traditional partners such as data science and AI teams, technology teams for the infrastructure, business teams who will use the system or data, governance and risk and compliance teams -- even researchers and customers," Baljevic says. Clark advises working across stakeholder groups. "Technology and business leaders, as well as practitioners -- from ML engineers to IT to functional leads -- should be included in the overall plan, especially for high-risk use case deployments," she says.
What happens when AI makes our bank’s products completely commoditized and undifferentiated? It’s not a defeatist question for the industry. Instead, it suggests a shortcoming in bank and credit union strategic planning about AI, Henrichs says. "Everyone’s asking about efficiency gains, risk management, and competitive advantages from AI," he suggests. "The uncomfortable truth is that if every bank has access to the same AI capabilities [and increasingly do through vendors like nCino, Q2, and FIS], we’re racing toward commoditization at an unprecedented speed." ... How can boards lead the institution to use AI to amplify existing competitive advantages? It’s not just about the technology. It’s "the combination of technology stack," say Jim Marous, Co-Publisher of The Financial Brand, with "people, leadership and willingness to take risks that will result in the quality of AI looking far different from bank A to bank Z. AI [is about] rethinking what we do. Further, fast follower doesn’t cut it because trying to copy… ignores the fundamental strategic changes [happening] behind the scenes." Creativity is not exactly a top priority in an industry accountable day-in and day-out to regulators, yet it’s required as technology applies commoditization pressure.?
To make educated choices as an entrepreneur, Scott and Stern recommend a sequential learning process known as test two, choose one for the four strategies within the compass. This is a systematic process where entrepreneurs consider multiple strategic alternatives and identify at least two that are commercially viable before choosing just one. As the authors write in their book, “The intellectual property and architectural strategies are worth testing for entrepreneurs who prefer to put in the work developing and maintaining proprietary technology; meanwhile, value chain and disruption may work better for leaders looking to execute quickly.” Scott referred to Vera Wang as a classic example of sequential learning. As a Ralph Lauren employee and bride-to-be at 35, Wang told her team that she felt there was an untapped market for older women shopping for wedding dresses. The company disagreed, so Wang opened her own shop — but she didn’t launch her line of dresses immediately. Instead, Scott said, Wang filled her shop with traditional dresses and offered only one new dress of her own. The goal was to see which types of customers were interested, as well as which aesthetics ultimately sold, before she started designing her new line. “[Wang] was able to take what she learned about design, customer, messaging, and price point and build it into her venture,” Scott said.
The real problem comes when speed means cutting corners - skipping tests, ignoring telemetry, rushing through code reviews. That might seem fine in the moment, but over time, it leads to tech debt and makes development slower, not faster. It’s kind of like skipping sleep to get more done. One late night? No problem. But if you do it every night, your productivity tanks. Same with software - if you never take time to clean up, everything gets harder to change. ... Software engineering productivity and sustainability are influenced by many factors and can mean different things to different people. For me, the two primary drivers that stand out are code quality and efficient processes. High-quality code is modular, readable, and well-documented, which simplifies maintenance, debugging, and scaling, while reducing the burden of technical debt. ... if the developers are not complaining enough, it’s probably because they’ve become complacent with, or resigned to, the status quo. In those cases, we can adopt the "we’re all one team" mindset and actually help them deliver features for a while – on the very clear understanding that we will be taking notes about everything that causes friction and then going and fixing that. That’s an excellent way to get the ground truth about how development is really going: listening, and hands-on learning.