Overhauling Government Technology During Perpetual Change

As a top target for attack, governments face constant threats, while struggling to deploy advanced tools and update compliance frameworks. These moving targets require government CIOs to be hypervigilant when it comes to adapting cybersecurity capabilities. In fact, by 2025, 75% of government CIOs will be directly responsible for security outside of IT, including operational and mission-critical technology environments.

In an adaptive security model, cybersecurity tools, techniques and talents merge and continually adjust to the threat landscape. Adaptive security is fundamental to creating trust that information is shared only as necessary and maintains its integrity. It is also fundamental to resilience in that it ensures information and services are available when needed. In an adaptive security model, cybersecurity systems are components for prediction, prevention, detection and response.

With threats increasing in scope and impact, cybersecurity and response capabilities must shift from a compliance-based to a risk-based approach. Cybersecurity techniques are only as good as the weakest link — very often, the human element — and require improved awareness programs and embedded cybersecurity practices throughout the organization.

The adaptive model forgoes traditional notions of perimeter and assumes there is no boundary for safe and unsafe. This is a necessary conceptual shift; while national policies often speak to data sovereignty, those kinds of boundaries provide limited operational security improvements. Cumulatively, the evolving threat, rapid advances in tools and updated compliance frameworks increase pressure on government agencies to evaluate their cybersecurity capabilities and embrace adaptive security.

Government decisions tend to have complex, interconnected effects — all of which impact government outcomes, often outside the purview of any agency. To improve trust and customer experience, the decision process must be clearly understood and emphasize delivering outcomes, not just adhering to process. Decisions must be based on accurate information in context.

The more mature areas of AI (computer vision, predictive analytics, machine learning) have proven to improve not only productivity and accuracy but decision making. AI for decision intelligence involves the systematic use of AI to achieve government missions more quickly, accurately and sustainably. This discipline informs decision making through an understanding of how decisions are made and how their outcomes are evaluated and improved by feedback. It applies to all major levels of decision types: one-off strategic decisions, managerial decisions and high-volume operational decisions.

AI is quickly taking off in commercial activities, and governments are expected to follow. This will lead to more “intelligent” interfaces for citizens and more efficiency in contact centers.

In preparation, establish a policy for governing the exploration and use of new tools, including generative AI. Articulate a vision of AI’s future business value and public benefit, and tie AI policies to desired outcomes. After implementation, track benefits and challenges from technical, operational and external viewpoints. Take the long view with a continuous assurance approach to policies and guardrails as systems and expectations evolve.

Government leaders are demanding more data for use in decision making and planning.

Government CIOs are responding by establishing programmatic data management ─ a systematic, scalable way to enable enterprisewide use of data assets. Data excellence is central to this approach, which enables data reuse, integrity, resilience and value through incentives and stewardship, standards and platforms, governance and sustainable innovation pipelines that produce actions.

Data excellence requires compromise, strong sponsorship and political leadership. Work with stakeholders, accepting risks in return for benefits and accepting responsibility for delivering value from data.

The pervasiveness of AI complicates these efforts, reinforcing the need for data management and integrity in data structures.

Government CIOs should:

• Improve data quality at scale with consistent rules and structures that are easy to follow and based on a transparent balance of benefit and risk.