How AI, ML, and Observability Can Transform SOCOM’s Intelligence Dominance

The US military must collect data streams from thousands of combat vehicles, environmental sensors and other intelligent devices across every military branch and quickly analyze data to drive split-second decision making. Nowhere is this more true than in Special Operations Management.

SOCOM organizes, trains, and equips specialist operators across all DoD military commands. It has a workforce of 70,000 and operates in the air, land and sea. Chief Digital and AI Officer Dan Folliard leads the command’s pathfinding data strategy, which focuses on using data, analytics and artificial intelligence to improve business operations and campaigning globally.

However, to realize this vision, a critical foundation is required: a high-performance, reliable and secure IT environment.

The good news is that SOCOM doesn’t have to rebuild or rebuild the sprawling Special Operations Forces Information Environment, or SIE, from scratch. Along with improving decision-making on the battlefield, AI and machine learning can also help IT leaders proactively address digital performance issues and achieve decision superiority.

Let’s explore key strategies to optimize SOCOM’s complex multi-domain SIE and advance its transition to a data-driven organization.

Full observability

Decision makers and warfighters need uninterrupted access to data at speed and scale. However, guaranteeing secure and seamless performance requires monitoring an exponentially huge number of disparate systems, edge devices, cloud and on-premises networks and technologies across the SIE.

Instead of piecemeal approaches to infrastructure monitoring, SOCOM should consider implementing network-wide observability. Observability differs from traditional monitoring because it requires a more proactive and holistic approach to network management. Through observability, IT professionals can assess the health and security of the entire SIE, including networks, applications, databases and infrastructure, and even hybrid environments.

With full-stack monitoring, IT teams across units, commands, and business functions can analyze islands of data, easily understand and visualize the big picture, and eliminate tool sprawl. They can observe device-to-device communications, properly understand data traffic, and detect anomalies. Additionally, observability automates and orchestrates critical monitoring and management tasks – a necessity in large and complex IT environments.

A key enabler of observability is Artificial Intelligence for IT Operations or AIOps.

AIOps deploys AI and ML to efficiently synthesize and analyze large volumes of data from multiple domains — a priority, but also key challenge for SOCOM. This results in fast and accurate performance analysis and troubleshooting. AIOps also provides actionable intelligence that enables teams to predict potential issues, such as network or application capacity utilization, before they occur and proactively guide remediation actions.

Thanks to ML’s continuous learning capabilities, AIOps also offers deep insights into the root cause of problems and can trigger mitigation workflows, allowing teams to focus on continuous optimization of the IT environment.

Data as a resource

For observability to be successful, SOCOM must ensure that its data-centric performance management approach aligns with DoD data governance principles – aka VAULTIS. VAULTIS means create data vit’s possible, oneaccessible, uunderstandable, largewith ink, trusty, Iinteroperable and smallinsure.

As SOCOM embraces observability, the following guiding principles can help:

— Support data governance practices to ensure security boundaries, data privacy, transparency and controlled access.

— Ensure data fidelity, including accuracy, completeness, consistency and timely availability across the entire IT stack, regardless of underlying technologies.

— Create relationships between workloads and views in dependencies. This helps provide full visibility into the network resources that support services, thereby reducing the blind spots associated with traditional network tools and improving the accuracy and speed of root cause analysis and troubleshooting.

Hybrid infrastructures

The use case for AI and ML within SOCOM are limitless, spanning the transformation of business operations to the improvement of campaigns worldwide. However, AI and ML, combined with other innovative technologies, also offer SOCOM a unique opportunity to elevate data to a strategic asset and rethink how it manages today’s hybrid architectures.

If SOCOM learns to harness the power of data, observability, and AIOps to proactively address SIE performance issues, the command can achieve a multi-domain infrastructure that is interoperable, reliable, transparent, secure, and high-performing. This, in turn, frees up teams to focus their efforts on developing cutting-edge applications and tools that leverage AI and ML in innovative and ground-breaking ways.

And it’s a repeatable design (or “gold standard”) that all DoD agencies can follow.

Krishna Sai is responsible for Artificial Intelligence for IT Operations or AIOps, service management and database portfolios at SolarWinds, an Austin, Texas-based company that develops enterprise software to help manage networks, systems and infrastructure their information technology.

Read the original at

See also  German submarines in Turkey will change the balance in the Southern Mediterranean

Related Posts