2026 Forecast - Things to Watch for This Year

As government agencies enter 2026, endpoint management and cybersecurity are no longer just technical disciplines: they are strategic enablers of mission continuity, national security, and digital sovereignty. Expanding remote/hybrid work, geopolitical tensions, AI acceleration, and tightening regulations are converging at the endpoint layer, where users, data, and devices intersect.

The coming year will force public sector organizations to rethink long-standing assumptions about trust, identity, software provenance, and even workforce legitimacy. Below are six areas that will materially shape endpoint management and cybersecurity strategies for government services in 2026.

Data Governance, Essential to Security Strategy

As federal agencies shift towards an increasingly distributed, cloud based, and data driven operating environment, data management has clearly emerged as a foundational pillar of IT security rather than a secondary governance concern. Traditional perimeter-based defenses are no longer sufficient when sensitive information is accessed across endpoints, mobile devices, SaaS platforms, and hybrid environments. Great strides have been made in securing endpoints through mature Mobile Device Management (MDM) and related technologies, but organizations have struggled to get a handle on strategically managing their data and digital communications.

Recognizing this reality, federal regulations and guidance such as the Federal Information Security Modernization Act (FISMA), Zero Trust aligned frameworks, and the Federal Data Strategy in M-19-18 place growing emphasis on data visibility, classification, and lifecycle control as prerequisites for effective security. These policies reflect a broader shift in federal thinking: agencies cannot protect what they do not understand, and securing infrastructure alone is meaningless if the data itself is unmanaged, misclassified, or overexposed. Requirements around data inventory, continuous monitoring, least-privilege access, and auditable controls all depend on agencies having a clear, consistent understanding of the sensitivity and usage of their information assets.

ISEC7 CLASSIFY helps government customers operate these mandates by embedding data awareness directly into day-to-day workflows and security controls. Through automated discovery, classification, and labeling of sensitive data across endpoints, email, and collaboration platforms, ISEC7 CLASSIFY provides the real-time visibility agencies need to align protections with policy intent. This enables more accurate classification marking, more effective enforcement of least privilege, stronger alignment with Zero Trust architectures, and faster, more precise incident response when security events occur. By reducing reliance on manual processes and user discretion, ISEC7 CLASSIFY also improves consistency and auditability, key requirements in today’s federal compliance environment. Ultimately, as federal security policy continues to move toward a data-centric model, ISEC7 CLASSIFY serves as a critical bridge between regulatory expectations and execution, helping agencies turn data management from a compliance obligation into a strategic advantage.

Localized AI

Localized AI When discussing AI today, many readers instinctively think of cloud-based services such as ChatGPT, Gemini, or Grok. These systems are general-purpose models hosted in hyperscale environments, designed to process large volumes of data centrally and deliver conversational or analytical outputs on demand. While highly visible, this category represents only one part of the AI landscape and is not representative of how AI is increasingly used in enterprise and government environments.

Localized AI follows a different model. In many cases, it is already deployed as on-device or edge-close intelligence embedded into managed endpoints, on-premises infrastructure, and sovereign cloud environments. Rather than continuously sending data to centralized platforms, these models run close to the data source—on mobile devices, desktops, industrial equipment, or constrained networks—with the objective of delivering precise, context-aware functionality within defined operational boundaries.

From an operational perspective, localized AI is designed for task-specific use cases that matter for security, compliance, and performance. On endpoints, this includes behavioral monitoring, detection of abnormal or risky activity, enforcement of security and compliance policies, performance optimization, and identification of issues like credential misuse or configuration drift. Because processing occurs locally, data movement is minimized, latency is reduced, and sensitive telemetry remains within governance boundaries.

The distinction from cloud hubs matters because not all “AI running locally” is equal. Recent developments in autonomous AI agents illustrate why this is so. Tools like Moltbot (formerly Clawdbot) show that AI models can indeed run with deep integration into local systems—enabling actions such as reading files, accessing credentials, and executing commands without human oversight. While such capabilities can be powerful, they also create an unbounded and insecure attack surface if not governed properly, with risks ranging from prompt-injection exploits to persistent memory poisoning and unauthorized system control.

Importantly, these risks are not architectural limitations of localized AI per se, but stem from how autonomous agents are designed and governed. Localized AI for enterprise use does not aim for unrestricted autonomy or unfettered access to system internals. Instead, it emphasizes controlled execution, policy enforcement, privilege separation, and human-in-the-loop governance. In other words, localized AI in a managed environment should have bound actions that align with organizational controls and compliance requirements.

Equally important is understanding what localized AI is not. It is not a conversational assistant, nor is it intended to replace large language models or provide open-ended reasoning capabilities. Localized AI does not continuously learn from global data sets or aggregate information across unrelated environments. Its value lies in predictability, speed, governance, and alignment with security and regulatory constraints, rather than flexibility or generative output.

For government agencies and regulated organizations, localized AI represents a practical and mature form of AI adoption rather than an emerging concept. It aligns with requirements around data protection, sovereignty, and operational resilience, particularly in environments with limited connectivity or heightened security constraints. Clarifying the distinction between cloud-centric AI, autonomous AI agents, and governed localized AI helps set realistic expectations and supports more informed decisions about how AI can be safely and effectively used on managed endpoints today.

Quantum computing 

“Modernization without considering PQC readiness or cryptographic agility is really creating technical debt in the future” Mike Duffy, Federal Chief Information Security Officer (CISO)

Quantum computing is no longer a distant theoretical concern; it is now firmly on the cybersecurity planning horizon for government services. At a recent congressional hearing, industry experts highlighted that nation-state actors are already harvesting encrypted data today with the explicit intent of decrypting it once quantum capabilities mature, a strategy known as “Harvest Now, Decrypt Later” (HNDL). This makes the threat of quantum-capable decryption both real and actionable, not some far-off future problem.

Quantum computers, when sufficiently advanced, will be able to break many of the public-key cryptographic algorithms that underpin secure communications and data protection, including RSA and ECC, because they can solve complex mathematical problems exponentially faster than legacy systems. This potential for cryptographic breakage puts government communications, stored classified data, and critical infrastructure at risk, if they remain reliant on legacy cryptography.

Government services should therefore pursue a strategic, architectural migration toward post-quantum cryptography (PQC) rather than viewing the challenge simply as an algorithm swap. Experts testified that agencies need crypto-agile architectures capable of rotating keys, updating algorithms, and deploying quantum-resistant cryptography without operational disruption or costly “rip-and-replace” projects. This includes strengthening existing encryption today while providing a clear path to integrating future PQC standards as they evolve.

The practical urgency is underscored by the fact that quantum computers capable of undermining mainstream cryptography may still be years away, but the preparation, testing, and migration effort across thousands of government systems is a complex, resource-intensive undertaking that demands early action. Agencies should inventory cryptographic dependencies across endpoints, identify systems with long-lived confidentiality requirements, and begin phased implementations of quantum-safe cryptographic standards and network protections.

In 2026, quantum readiness will be defined by proactive modernization rather than reactive scrambling. Endpoint and identity teams will need to work closely with PKI owners, network architects, and risk officers to ensure not only that cryptography is quantum resilient, but that encryption, authentication, and key management systems remain agile, verifiable, and interoperable across government ecosystems.

SBOM and Supply Chain Concerns

Software supply chain security has continued to be concern and addressing the Software Bills of Materials concerns are no longer just about vulnerability management, they are about trust, accountability, and operational continuity.

Endpoints today consume software from a complex ecosystem of operating systems, drivers, agents, SaaS clients, and third-party libraries. SBOMs provide visibility into these components, enabling agencies to quickly determine exposure when a vulnerability or compromise is disclosed upstream. This is especially critical in government environments, where patching delays or uncertainty can translate directly into mission risk.

That said, SBOM adoption will face growing pains. Agencies will need to deal with inconsistent formats, incomplete vendor disclosures, and limited automation for ingesting and correlating SBOM data with endpoint inventories. In 2026, mature organizations will move beyond “SBOM collection” toward continuous supply chain monitoring, correlating SBOM data with runtime behavior, device posture, and threat intelligence.

ISEC7 SPHERE is already aligned with this evolution by early adopting SBOM monitoring capabilities as a natural extension of its existing CVE and exposure monitoring approach. Just as SPHERE continuously tracks known vulnerabilities across endpoints, it can ingest and analyze SBOM data to understand which software components and third-party libraries are present on managed devices. This allows organizations to move from abstract risk awareness to concrete, device level impact assessment.

By knowing exactly which libraries, frameworks, and dependencies are used by installed software, SPHERE helps security and endpoint teams rapidly identify affected endpoints when a new vulnerability, supply chain compromise, or malicious dependency is disclosed. Instead of relying solely on vendor advisories or static SBOM files, agencies gain continuous visibility into software composition and can monitor changes over time, linking supply chain risk directly to real-world endpoint exposure.

This SBOM driven visibility supports a pragmatic, operational approach to supply chain security. It enables government services to prioritize remediation based on actual usage and risk, reduce blind spots created by incomplete disclosures, and integrate supply chain monitoring into the same workflows already used for vulnerability management, compliance, and incident response.

Banned Application and Endpoint Ownership Models

While TikTok may appear tangential to endpoint management, it serves as a useful lens to discuss device ownership models and control levels in government cybersecurity policy in 2026. Ongoing concerns around data collection, algorithmic influence, and foreign jurisdiction have already led many agencies to restrict or ban high-risk consumer apps on official devices, highlighting differences between Corporate-Owned, Personally Enabled (COPE) and Bring Your Own Device (BYOD) models.

In COPE environments, agencies retain strong controls over installed applications, network access, and data flows, making enforcement of allowlists, blocklists, and other security policies straightforward. By contrast, BYOD devices present more limited control, relying on endpoint management agents, containerization, and monitoring to enforce policy while respecting user privacy. This contrast underscores why risk-based application governance must be tailored to the device ownership model.

Unified Endpoint Management (UEM) solutions provide the central architecture for enforcing these controls consistently across COPE and BYOD devices. Integrated with platforms like ISEC7 SPHERE, UEM data feeds can extend visibility beyond configuration and policy compliance into runtime behavior, application usage, and potential data leakage paths. SPHERE can aggregate telemetry from all endpoints, correlate it with allowlists, blocklists, and SBOM monitoring, and provide a unified view for security, compliance, and risk officers.

By combining UEM policy enforcement with SPHERE’s continuous monitoring, government services can ensure that high-risk applications, shadow usage, and compliance gaps are visible and actionable across all endpoint types. This approach allows agencies to assert security, privacy, and operational directives in practice, regardless of whether the device is COPE or BYOD, and to maintain a consistent, agency-wide stance against emerging threats and politically sensitive software exposures.

Conclusion 

As we look ahead to 2026, one truth stands out: endpoints are now the front line of enterprise security. For organizations navigating an increasingly distributed, device‑driven world, the ability to secure data, identities, and applications—wherever work happens—will define their resilience. Localized AI will accelerate decision‑making at the edge, but it will also demand smarter, more adaptive controls. Quantum‑resilient architectures will shift from long‑term planning to near‑term necessity. SBOM visibility and supply‑chain integrity will become foundational expectations for every enterprise that depends on third‑party software.