Investment firms are under growing pressure to deliver software faster, as trading support tools, analytics platforms, investor portals, internal dashboards, and workflow applications are now living, dynamic products. They need to evolve continuously as markets shift, client expectations change, and firms adopt more advanced forms of automation and AI. Firms must integrate AI-driven DevSecOps (Development, Security, and Operations) into the software development process if they are to keep pace.
The cost of failure remains high. The average data breach in the financial industry has reached $6.08 million, IBM reports, making it one of the most expensive sectors for companies that get security wrong. That combination of pressure and exposure is changing the way firms need to think about application delivery. The old model—build quickly, test late, review at the end, and remediate after the fact—creates too much friction and too much risk.Â
For investment firms, AI-driven DevSecOps is a disciplined operating model for building, releasing, and maintaining software in a way that supports speed, security, and compliance at the same time.
Investment firms are becoming software businesses
Many investment firms still think of software as a support function. In practice, it has become part of the business model. Firms depend on software to support multiple operations, including:
- research workflows
- portfolio analytics
- client reporting
- risk monitoring
- trade support
Increasingly, software provides a differentiated client experience, helping investment firms achieve a competitive advantage without substantial additional manual work.
That shift becomes more important as AI adoption expands. NVIDIA’s 2026 financial services industry survey found that 65% of firms are actively using AI, up from 45% the previous year, and 61% are using or assessing generative AI. As AI moves deeper into business workflows, firms are not updating more logic, connecting more systems, and introducing more model-driven capabilities into applications that matter operationally.Â
This means technology delivery is now a business capability. When an investment firm cannot update internal tools quickly, harden client-facing systems efficiently, or move new functionality into production without lengthy delays, it slows down both the technology team and the business.
Speed without embedded security does not scale
Many firms have already recognized the need for faster release cycles. The problem is that they often try to accelerate delivery without changing how security and compliance are built into the process. That usually leads to predictable results: more friction at the end of the cycle, more rework before release, and more tension between development, infrastructure, security, and risk teams.
This is the core operational weakness DevSecOps is meant to address. Rather than treating security as a separate checkpoint after development is mostly complete, DevSecOps pulls security and control activities into the workflow from the beginning. That makes the review more continuous, more automated, and better aligned with how modern applications are actually delivered.
The market is already moving in this direction. Practical DevSecOps reports that 36% of organizations now develop software using DevSecOps, up from 27% in 2020. A shift from traditional release models that struggle to support both software and risk management may be driving that change.
For investment firms, this point is especially important. The systems being updated may influence reporting, investor experience, workflow quality, or time-sensitive operations. In that environment, release discipline contributes to preserving trust, continuity, and control.
What AI-driven DevSecOps actually changes
DevSecOps already improves delivery by integrating development, security, and operations more tightly. AI-driven DevSecOps adds another layer: it uses AI to reduce manual bottlenecks inside that operating model.
In practical terms, that may include using AI to help identify vulnerable code patterns, prioritize security findings, generate or improve test coverage, detect configuration issues, summarize changes, or support incident investigation. None of that removes human accountability. It reduces the amount of low-value manual effort required to maintain quality and control as release frequency increases.
That is a meaningful change for mid-market investment firms. Many do not have the engineering headcount or internal specialization of larger banks or fintechs. They need ways to improve release discipline without building oversized internal teams. AI can help close that gap when used inside a structured process rather than as an informal add-on.
This is also where expectations should stay realistic. AI-driven DevSecOps is about making pipelines smarter, surfacing issues earlier, and giving teams better leverage with humans in the loop. The firms that benefit most will be the ones that use AI to strengthen process quality.
Compliance has to move into the pipeline
In financial services, secure software delivery is about engineering quality, but also about evidence, accountability, and repeatable controls. Investment firms operate in an environment where system changes can affect several areas, including:
- client data
- records
- reporting quality
- supervisory processes
- operational resilience
That means compliance cannot sit outside the delivery lifecycle as a downstream review function.
This is why continuous compliance automation is becoming more important. Gartner projects that by 2028, 65% of organizations will have integrated compliance automation into DevOps workflows, and that AI will power 75% of those processes. The implication is clear: the workflow itself is becoming the control point.
For investment firms, that means the software pipeline must increasingly produce auditable evidence as work moves forward. Security checks, policy enforcement, dependency scanning, approval paths, and testing records need to become part of normal delivery rather than special exercises performed right before release or during an audit response.
This is one of the most important mindset changes for leaders outside engineering. DevSecOps is a way to create better visibility into how software changes are governed. When done well, it supports both operational agility and stronger control.
A practical AI-driven DevSecOps model for investment firms
Most investment firms do not need to transform every application at once. A better approach is to begin with the systems where delivery speed, business importance, and control requirements intersect most clearly. That may be a client reporting portal, an internal operations dashboard, a workflow application that supports onboarding or compliance, or a trading-adjacent support tool with frequent release needs.
From there, the operating model should focus on a few priorities:
Start with business-critical applications
The first candidates should be applications that already matter to performance, service quality, or operating efficiency. That gives the firm a clearer business case and makes it easier to measure progress in terms leaders care about, such as release time, issue rates, recovery time, or workflow improvement.
Build controls into the lifecycle
Security and compliance checks should move closer to development and deployment, rather than being isolated at the end. That includes code scanning, dependency monitoring, approval logic, logging, and policy checks that can be applied consistently across releases. The goal is to make control part of the delivery itself.
Use AI to remove friction, not judgment
AI works best where it reduces repetitive effort and helps teams act sooner. It can help identify patterns, flag likely issues, and improve triage. It should not become a substitute for ownership over architecture, risk decisions, or release accountability.
Treat DevSecOps as an operating model
This is where many firms go wrong. They buy tools, but do not change how teams work together. Effective DevSecOps depends on clearer coordination between application owners, infrastructure teams, security teams, and firm leadership. The process has to be designed as a shared discipline, not layered onto siloed teams that still operate independently.
The firms that benefit most will be the most disciplined
There is a tendency to describe AI-driven software delivery in very expansive terms. In reality, the biggest gains usually come from firms that take a narrower and more structured approach. They identify the applications that matter most, improve the controls around them, automate what is repeatable, and expand from there.
That matters in investment management, where the goal is to improve how the firm operates while protecting the trust that clients, counterparties, and regulators expect. Faster software releases are valuable, but only if they come with visibility, resilience, and accountability.
For that reason, AI-driven DevSecOps should be seen less as a trend and more as a maturity step. It reflects a more realistic understanding of how modern software needs to be built in regulated environments. Firms that embrace it thoughtfully will be better positioned to deliver new capabilities without letting security and compliance become recurring bottlenecks.
Partner with Option One Technologies on AI and DevSecOps
Option One Technologies helps investment firms build the infrastructure, cybersecurity posture, and operating foundation required to support modern application delivery in regulated environments. For firms that need to improve release speed without losing visibility or control, the right approach often requires better cloud architecture, stronger cybersecurity operations, resilient infrastructure, and an IT partner that understands the realities of investment management. Contact a team member today to learn more.
