Date: 8 July 2026

Most commodity firms have a serious sequencing problem. For many commodity firms, modernization is no longer just an IT initiative. It is also a cybersecurity and operational resilience priority. Legacy trading platforms often sit at the heart of critical business operations, making them attractive targets for cybercriminals while simultaneously creating challenges for incident response, regulatory compliance and cyber resilience.

The trading platform that tracked positions across a dozen desks in 2012 now sits at the center of a much more complex operation: multi-commodity portfolios, real-time data feeds, compliance layers, and an analyst team that wants answers before the market closes.

The system works, mostly. But it can’t support the reporting stack the business needs, can’t talk cleanly to modern APIs, and can’t run the AI models the team keeps requesting. Replacing it entirely feels like the right answer until someone runs the project risk numbers. A full platform replacement in a live trading environment can take 18 to 36 months, requires parallel operations, and carries the kind of delivery risk that ends careers.

From a cybersecurity perspective, wholesale platform replacement also introduces additional risks. Large-scale migrations can create temporary security gaps, increase the attack surface and make it harder for organisations to maintain visibility across systems. A phased approach often allows security controls to evolve alongside the technology, reducing operational and cyber risk.

So most firms stay stuck. Explore what you can do with it: https://www.altamira.ai/artificial-intelligence/large-language-models/

They build workarounds, and defer the bigger decision. Meanwhile, competitors with more flexible infrastructure move faster on deals, close positions with better information, and get to regulators first.

There is a third path: staged modernization that targets the layers of highest business value without touching the core system until you’re ready or until you decide you don’t need to.

Why Trading Platforms become Modernisation Bottlenecks

Legacy CTRM and ETRM systems were built for a different type of trading operation. As EPAM’s analysis notes, they were built for predictable sequences across single-commodity portfolios where overnight batch valuation was acceptable. Today, that model breaks down fast.

The numbers reflect the gap. According to Molecule Software’s 2025 ETRM/CTRM Transformation and Modernization Report, which surveyed more than 400 companies across 10+ industries, 64% of respondents say their current ETRM systems still don’t support all their processes or traded commodities. In 2024, 66% of firms were still in the planning stage of modernization, only 24% had an active initiative underway.

These peripheral failures accumulate into a real delivery risk. A platform that can’t surface risk data in real time, can’t push clean feeds to downstream analytics tools, or can’t support automated compliance checks becomes a constraint on every project that touches it.

Legacy environments also present significant security challenges. Unsupported software, outdated authentication methods, fragmented logging and limited integration with modern security monitoring tools can all make it more difficult to detect and respond to cyber incidents. As regulations increasingly require organisations to demonstrate cyber resilience, these limitations become business risks rather than purely technical concerns.

Which platform layers can be modernised in stages

Not every part of a legacy trading platform ages at the same rate. Some components like core position management, settlement logic, deal capture are stable and deeply embedded in the operational process. Others are more exposed to change pressure and can be targeted independently.

Reporting and analytics

Reporting is usually the first and most visible bottleneck. Teams export data manually, build reports in spreadsheets, and re-run calculations that the platform should handle automatically. This layer can be decoupled from the core system by building a dedicated data layer that pulls from the existing database, normalizes position data, and feeds it into modern BI tools or custom dashboards.

Modern reporting capabilities also improve cyber incident response. Security, compliance and executive teams need rapid access to reliable operational data during an incident. Better reporting enables faster decision-making, clearer regulatory reporting and improved communication with internal and external stakeholders.

Workflow orchestration

Trading operations involve a lot of handoffs between risk and compliance, between execution and settlement, between front-office and back-office teams. In legacy environments, these handoffs often rely on manual steps, shared inboxes, or spreadsheet trackers sitting outside the platform.

Orchestration layers can be built alongside the existing system to formalize these handoffs, add approval gates, trigger notifications, and create audit trails without modifying the underlying CTRM or requiring a data migration.

These orchestration layers can also strengthen cyber resilience by embedding security approvals, incident escalation paths and documented response procedures directly into operational workflows. This helps organisations reduce manual errors while improving consistency during high-pressure situations.

External integrations

Building a clean API or middleware layer between the platform and external systems creates a stable integration surface that can be updated independently. When a data provider changes their feed format, or a new regulatory reporting requirement comes in, you update the integration layer, not the platform.

Secure API design should be a priority throughout modernization. Authentication, encryption, monitoring and access controls should be incorporated into every integration to reduce the risk of exposing critical trading or operational data.

How LLM and AI layers can support legacy environments without full replacement

One of the more useful developments of the last two years is that AI capabilities no longer require a modern underlying platform to deliver value. LLMs and AI tooling can be layered on top of legacy systems in ways that don’t require a data migration or architecture overhaul.

The most practical patterns for commodity trading environments include the following:

• Natural language querying over exported data. An LLM-backed interface can allow traders and analysts to query position data, exposure summaries, or historical trade records in plain language, even when the underlying data comes from flat files or legacy database exports.
• Document and contract analysis. Physical commodity trading involves substantial documentation. AI models can extract structured data from these documents and surface it in workflows without needing the legacy platform to support document ingestion natively.
• Anomaly detection and risk flagging. An AI monitoring layer can sit upstream of the legacy risk engine, flagging unusual position changes, data quality issues, or threshold breaches before they reach compliance review.
• Report generation and narrative summarization. Instead of exporting data and writing risk summaries manually, AI layers can draft structured reports from platform data, reducing analyst time on low-value production work.

None of these require replacing the platform. They require a stable data extraction path from the existing system and a disciplined approach to integration design, which is exactly where modernization effort should focus first.

However, organisations should ensure AI initiatives are introduced within an appropriate governance framework. Sensitive trading data, commercially confidential information and regulatory obligations require robust controls around data access, model usage, logging and human oversight. AI can accelerate operations, but only when deployed securely.

How Altamira supports modernization for complex software environments

Commodity trading platforms are not generic enterprise software. They carry years of configuration, firm-specific business rules, and integrations that are rarely well-documented. Modernization decisions made without a detailed understanding of that complexity tend to go wrong in expensive ways.

Discovery and prioritization

Before proposing any architecture changes, our team runs a structured discovery process to map the current platform’s data flows, integration points, and operational dependencies. The goal is to identify which components are genuinely stable, which are under active pressure, and where incremental changes will produce measurable business impact.

This stage produces a prioritized modernization roadmap and a sequenced set of interventions ranked by business value and delivery risk. It gives CTOs and product owners a clear picture of what they can change now, what requires more groundwork, and what is better left alone.

A comprehensive discovery phase should also include a cybersecurity assessment. Identifying legacy vulnerabilities, privileged access risks, unsupported components and existing security controls provides a clearer understanding of where modernization can reduce both operational and cyber risk.

Controlled implementation planning

Altamira’s implementation approach for legacy environments is built around containment: changes are scoped to avoid touching stable core systems until the risk profile of doing so is well understood. New components are built to run alongside existing systems before replacing them, and rollback paths are defined before development starts.

A data pipeline failure during a high-volume session is not a recoverable situation. The implementation plan needs to account for that from the start.

Security validation should form part of every implementation phase. Penetration testing, vulnerability assessments, access reviews and tabletop exercises help verify that new integrations and services improve resilience rather than introduce additional exposure.

Practical guidance for CTOs and product owners

If you’re evaluating a modernization initiative on a legacy trading platform, a few principles tend to separate projects that deliver from those that stall:

• Start with the data layer. Most downstream improvements like better reporting, AI tooling, external integrations depend on having a reliable, queryable data extraction path from the existing system. Getting that right is foundational.
• Separate what the business needs from what IT wants to fix. Legacy platforms often carry technical debt that developers want to resolve but that has no direct business impact. Scope modernization around what will change outcomes for trading, risk, or compliance teams.
• Treat integration surfaces as products. Every point where your platform connects to an external system is a maintenance liability. Building clean, versioned APIs at these boundaries reduces long-term cost and makes future changes easier to absorb.
• Define what “done” means before you start. Modernization projects without clear success criteria tend to expand. Set measurable targets, like reporting latency, manual process reduction, integration reliability, and use them to evaluate progress and scope new work.
• Build cyber resilience into every modernization milestone. Modernization should strengthen security as well as functionality. Incorporate secure architecture reviews, incident response planning, backup validation and resilience testing throughout the programme rather than treating cybersecurity as a final-stage activity.

The firms that move well on this tend to have one thing in common: they treat it as an operational risk management decision, with the same discipline they’d apply to a new market position.

For organisations operating in regulated sectors, modernization should also support broader resilience objectives. Whether aligning with operational resilience programmes, cyber incident response plans or evolving regulatory expectations, technology investments should improve an organisation’s ability to prepare for, respond to and recover from cyber disruptions.

Conclusion

Commodity trading value pools fell more than 30% year over year in 2024, and margins are unlikely to recover quickly. Firms that can’t surface risk data in real time, can’t support modern reporting workflows, or can’t integrate new data sources without a multi-month IT project are at a structural disadvantage.

Full platform replacement is the right answer for some organizations. But for most, the smarter path is staged modernization that targets the layers creating the most friction, without the delivery risk of replacing a working core system under live trading conditions.

Altamira helps commodity firms answer that question with a structured discovery process that maps dependencies, identifies high-value targets, and builds a sequenced modernization plan designed around your operational constraints. Get in touch to discuss your platform environment.

Successful modernization programmes balance innovation with resilience. By treating cybersecurity as a core design principle rather than an afterthought, organisations can improve operational efficiency while strengthening their ability to withstand cyber threats, regulatory scrutiny and business disruption.

For many commodity firms, modernization is no longer just an IT initiative. It is also a cybersecurity and operational resilience priority. Legacy trading platforms often sit at the heart of critical business operations, making them attractive targets for cybercriminals while simultaneously creating challenges for incident response, regulatory compliance and cyber resilience.