Securing AI, cloud, and OT environments without slowing innovation

Security can no longer operate as a gate that modernization passes through. It has to be built into how modernization happens.

AI workloads are moving into production. Cloud environments span multiple providers and on-premises infrastructure. OT systems that once ran in isolation are now connected to enterprise networks. Each shift expands the attack surface faster than most security programs were designed to handle.

According to Accenture's State of Cybersecurity Resilience 2025, 63% of organizations have limited cyber readiness and a reactive posture to threats. Only 10% have reached an adaptive, continuously evolving security posture. That gap is not primarily a technology problem. It is a design and integration problem.

The IBM 2025 Cost of a Data Breach Report makes the case for getting it right: organizations using AI and automation extensively in security operations saved an average of $1.9 million per breach and reduced the breach lifecycle by 80 days. The advantage belongs to organizations that build security in from the start, not those that add it afterward.

The three environments where security gaps are growing fastest

AI systems without governance controls

AI adoption has moved faster than AI security. The IBM 2025 Cost of a Data Breach Report

found that 63% of breached organizations had no AI governance policy in place or were still developing one. Of organizations that experienced an AI-related breach, 97% lacked proper AI access controls at the time.

Shadow AI is a significant contributor. One in five breaches involved shadow AI, where employees adopted unauthorized tools without IT oversight, adding an average of $670,000 to breach costs and disproportionately exposing customer data and intellectual property.

The security gaps most commonly left unaddressed:

• AI models and applications without inventory, access controls, or monitoring
• Shadow AI tools operating outside IT visibility with access to sensitive data
• AI supply chain exposure through third-party APIs, plugins, and integrations
• Insufficient data classification for the datasets AI systems process

Cloud environments spanning multiple providers

Breaches involving data distributed across multiple environments cost an average of $5.05 million, compared to $4.01 million for on-premises breaches, according to IBM's 2025 Cost of a Data Breach Report. The gap reflects the difficulty of detecting and containing incidents across environments with inconsistent controls, fragmented visibility, and different identity configurations.

The Palo Alto Networks 2025 State of Cloud Security Report, drawing on insights from over 2,800 security leaders, found that 99% of organizations experienced at least one AI-related security incident in the past year. Development velocity compounds the exposure: 52% of teams release code weekly, but only 18% can remediate vulnerabilities at the same pace.

OT environments at the IT/OT boundary

OT systems running industrial equipment, utility infrastructure, and manufacturing processes were historically air-gapped from enterprise IT. That isolation is largely gone. As organizations connect OT environments to enterprise networks for analytics, remote monitoring, and operational optimization, the attack surface at the IT/OT boundary has grown significantly.

OT systems frequently run legacy software that cannot be patched on standard IT timelines, and downtime in these environments carries safety and reliability consequences that go beyond typical IT risk. Security frameworks built for cloud do not transfer directly to OT without meaningful adaptation in segmentation, monitoring, and incident response.

What embedding security into modernization actually requires

Treating security as a foundation for modernization rather than a gatekeeper requires changes to how security is positioned, resourced, and integrated into delivery. Four practices define what that looks like in environments undergoing continuous change.

Establish governance before scaling

The most expensive security outcomes follow a consistent pattern: capabilities are deployed, governance is established afterward, and by the time controls catch up, exposure has already accumulated.

Effective governance for AI, cloud, and OT includes:

• Clear ownership and accountability across technology and business teams

• Defined AI deployment and access policies before tools go into use

• Consistent security baselines applied across cloud environments through automation

• Regular audits that surface shadow AI, misconfigurations, and access gaps before they become incidents

Adopt identity-first and zero trust principles

Perimeter-based security assumes users, devices, and data have known locations. That assumption does not hold in distributed cloud environments, hybrid OT/IT architectures, or workplaces where AI tools can be adopted without IT involvement.

Zero trust, where access is continuously verified regardless of network location or prior authorization, provides a more durable foundation. This applies to human identities, machine identities, and the non-human identities AI systems generate as they interact with APIs and data pipelines. Identity has become the primary attack vector, and phishing-resistant authentication is among the highest-return investments available in complex, distributed environments.

Integrate security into delivery workflows

Security reviews at the end of a delivery cycle find problems after the cost of fixing them has grown. Integrating security from the start, through secure configuration standards, automated compliance checks in development pipelines, and continuous monitoring, produces faster detection and lower remediation costs without slowing delivery.

Build continuous monitoring across every environment

Static security postures degrade over time. Configurations drift. AI models are updated in ways that change their risk profile. New tools get adopted outside of IT oversight. Continuous monitoring across cloud, AI, and OT environments surfaces these changes before they become exploitable gaps. In OT environments where patching is constrained, monitoring is often the primary mechanism for early detection.

Security as a condition for sustained progress

The cost of reactive security extends beyond the financial. It is operational disruption, lost stakeholder trust, and modernization momentum lost to incidents that better governance would have prevented. Organizations that embed security rigorously and early spend less time managing fallout and more time moving forward.

Security embedded by design does not constrain progress. It is what makes sustained progress possible.

TSG embeds cybersecurity into every phase of modernization across cloud, data, AI, and OT environments. From governance and compliance to threat detection and identity, our integrated approach ensures security enables progress rather than constraining it.