How a payment platform is bringing AI into its systems, workflows, and products securely, so that capabilities, security, and resilience advance together.
The exponential improvements AI is driving in payment platforms are remarkable. The technology is maximising transaction outcomes through optimisation for businesses that use platforms like Praxis to manage their global payments, while simultaneously playing a significant role behind the scenes within the payment engine that delivers those gains. Our focus has been on selectively integrating AI where we believe it will create real operational value, while being mindful about where it should not be trusted or fully automated, especially in payment infrastructure.
As AI plays a larger role in how all software is written, reviewed, and managed, the way it is brought in has to be deliberate, given that the data it handles is sensitive, the output it produces drives real decisions, and the platform it operates within carries the responsibility of managing live payment activity at scale. At Praxis, that work is happening today across all our systems, team workflows, and the products our merchants use.
Building the foundations
In our case, the approach to bringing AI into a platform safely starts with three areas where careful consideration carries the most weight.
The first is safely managing access to the data and systems that AI works with. This includes internal company knowledge and, when AI is implemented directly into our platform, governing how it accesses customer information and payment data. The same standards that govern any other handling of that data apply here, with additional practices specific to how AI tools store, process, and reference what they are given.
The second is the output. Our integration of AI into the platform means it can already read and understand the performance of a merchant's processors and intelligently recommend alternative routing configurations that would improve their first-time approval rates. This output has been engineered to reflect the actual data behind those suggestions every time and has been built in a way that ensures AI-generated outputs remain explainable, reviewable, and bounded by operational controls rather than treated as a black box.
The third is the review process around it all. For instance, AI work at Praxis is treated like any other engineering change to the platform, with the same supervision, checks, and accountability that any other change would receive. This ensures that AI interacts only with the relevant internal systems and does not operate with excessive permissions. The result is AI-assisted workflows that support our development and review processes, protecting against AI-generated code introducing insecure dependencies or hidden business logic issues.
For the Praxis platform, this discipline extends to the AI ecosystem that the platform itself relies on. External models, AI plugins, and AI-assisted tooling are treated with the same scrutiny applied to any other third-party component.
How AI is already strengthening how we run security
In parallel with the foundations work, AI is making us more resilient to malicious actors. Cybersecurity at this scale produces a large and complex set of signals to manage, and AI models are supporting us to consolidate those signals so that what matters most surfaces faster and is acted on with speed. This includes prioritising the alerts that matter, correlating activity across cloud and identity systems, and reducing the investigation noise.
This is happening in step with how the cybersecurity industry as a whole is advancing its own use of AI. As detection and response capabilities sharpen across the field, we continuously align our procedures alongside those advancements so that prevention, detection, and response all benefit from the same momentum, producing a cybersecurity programme that gets stronger as the industry around it does. The result is a programme that is more resilient against incidents as they arise, more efficient in how it identifies and contains them, and more capable of reducing the blast radius of any single issue.
The foundations work and the cybersecurity work are not separate efforts but two parallel streams within the same broader programme of leveraging AI capabilities at Praxis. The infrastructure being engineered today is one where AI is brought in safely, used responsibly, and given access to systems where it can make the most difference.
What this opens up is significant. The work happening today is already shaping how the Praxis platform operates. It is also setting the foundation for the larger role AI will play in the years ahead, from how the code that builds the platform is written, to the capabilities offered through it, to how merchants interact with our orchestration engine and use it to run their payment operations and outcomes more intuitively than has been possible before.
The conversation about what AI can do will continue to accelerate, and the parallel work of building the foundations behind it will keep pace, so that capabilities, security, and resilience develop together.
About the author
As Chief Information Security Officer at Praxis Tech, Ad Attias shapes how Praxis embeds security into its payment orchestration platform, ensuring transaction integrity, platform resilience, and the protection of sensitive payment data. Ad brings more than 15 years of experience in cybersecurity, spanning secure product development, security architecture, and managed security services across payments and financial infrastructure.
About Praxis Tech
Praxis Tech provides payment orchestration infrastructure for merchants operating globally. Through a single integration, the platform connects businesses to hundreds of PSPs, acquirers, and alternative payment methods. Praxis enables intelligent transaction routing, payment optimisation and unified control over providers and payment performance, helping merchants improve approval rates and manage payments across multiple markets from one platform. Learn more at praxis.tech.
