A recent CISA red team assessment of a United States critical infrastructure organization revealed systemic vulnerabilities in modern cybersecurity. Among the most pressing issues was a heavy reliance on endpoint detection and response (EDR) solutions, paired with a lack of network-level protections.

These findings underscore a familiar challenge: Why do organizations place so much trust in EDR alone, and what must change to address its shortcomings?

EDR’s double-edged sword

A cornerstone of cyber resilience strategy, EDR solutions are prized for their ability to monitor endpoints for malicious activity. But as the CISA report demonstrated, this reliance can become a liability when paired with inadequate network defenses. Here’s why:

1. Tunnel vision on endpoints: EDR excels at identifying threats on individual devices but struggles with network-wide attacks. This leaves gaps when hackers exploit lateral movement or unusual data transfers — activities that often require network-level visibility to detect.
2. Playing catch-up with threats: Traditional EDR tools depend on recognizing known indicators of compromise (IOCs). Advanced attackers can easily sidestep these tools by using novel techniques or blending in with legitimate activity.
3. Blind spots in legacy systems: Legacy environments often go unnoticed by EDR, giving attackers free rein. In the CISA case, these systems allowed the red team to persist for months undetected.
4. Overwhelmed defenders: Even when EDR generates alerts, security teams can become desensitized by a flood of notifications. As seen in the CISA assessment, critical warnings can slip through the cracks simply because defenders are too stretched to respond.

Common EDR pain points

The challenges highlighted in the CISA report mirror broader issues organizations face with EDR:

• Detection without context: EDR tools often spot anomalies on endpoints but fail to connect the dots across the broader network. This lack of context can leave organizations blind to coordinated attacks.
• Weak network integration: Without network-layer defenses, EDR struggles to identify malicious activities like unusual traffic patterns or data exfiltration, key tactics in advanced breaches.
• Fragmented systems: Many organizations operate a patchwork of security tools, leaving critical gaps in coverage and making it harder to correlate data across endpoints, networks and cloud environments.

The next evolution of EDR

Recognizing these shortcomings, cybersecurity is rapidly evolving beyond traditional EDR. Here’s how:

1. Extended detection and response (XDR): XDR takes EDR to the next level by integrating endpoint, network and cloud data into a single platform. This broader scope allows organizations to see the full attack picture and respond more effectively.
2. AI-driven insights: Cutting-edge EDR solutions now harness machine learning to detect subtle behavioral anomalies. By identifying deviations from normal activity, these tools catch threats even when no IOCs exist.
3. Zero trust security: Zero trust architectures take endpoint defense a step further by ensuring no device or user is trusted by default. This integration of endpoint, identity and network security reduces dependence on EDR alone.
4. Network visibility: Modern EDR tools are incorporating network traffic analysis to close the gaps identified in the CISA report. Monitoring traffic for anomalies, such as unusual data flows or external connections, bolsters defenses.
5. Cloud-native solutions: As businesses embrace hybrid and cloud environments, EDR is evolving to provide seamless coverage across on-premises and cloud systems, addressing vulnerabilities in these critical areas.

Why do gaps persist?

Even with these advancements, many organizations struggle to fully address EDR’s limitations:

• Resource strains: Small security teams often lack the bandwidth or expertise to implement and manage advanced solutions like XDR.
• Budget constraints: Upgrading to integrated platforms or modernizing legacy systems can be costly.
• Legacy challenges: Outdated environments remain vulnerable, acting as weak points that attackers can exploit.
• Leadership missteps: As the CISA report pointed out, organizations sometimes deprioritize known vulnerabilities, leaving critical gaps unaddressed.

Building a more resilient future

The CISA red team findings are a wake-up call: Endpoint protection alone is no longer enough. To outsmart today’s sophisticated adversaries, organizations must adopt a layered defense strategy that integrates endpoint, network and cloud security. Solutions like XDR, zero trust principles and advanced behavioral analysis offer a path forward — but they require strategic investments and cultural shifts.

The post Insights from CISA’s red team findings and the evolution of EDR appeared first on Security Intelligence.

At the end of 2024, we’ve reached a moment in artificial intelligence (AI) development where government involvement can help shape the trajectory of this extremely pervasive technology.

In the most recent example, the Department of Homeland Security (DHS) has released what it calls a “first-of-its-kind” framework designed to ensure the safe and secure deployment of AI across critical infrastructure sectors. The framework could be the catalyst for what could become a comprehensive set of regulatory measures, as it brings into focus the significant role AI will play in securing key infrastructure systems.

As Secretary Alejandro N. Mayorkas put it, “AI offers a once-in-a-generation opportunity to improve the strength and resilience of U.S. critical infrastructure, and we must seize it while minimizing its potential harms. The framework, if widely adopted, will go a long way to better ensure the safety and security of critical services that deliver clean water, consistent power, internet access and more.”

Mayorkas’ statement underscores the urgency of getting it right, as today’s decisions will profoundly shape how AI impacts vital systems in the future.

Key features of the DHS AI framework

The framework lays out clear roles and responsibilities for the parties involved in AI development and deployment for critical infrastructure.

Risk management guidance: DHS suggests an approach that incorporates ongoing risk management, advising stakeholders to continually identify, assess and mitigate potential AI risks. The recommendation includes adopting transparent mechanisms to track AI decisions that could impact essential services.

Ethical standards for developers: The guidelines stress the importance of incorporating ethical considerations into AI design, and make a push for responsible practices that minimize harm and ensure equitable treatment.

Collaboration across sectors: Recognizing the interconnected nature of infrastructure, DHS is promoting collaboration between public and private sectors to share best practices and vulnerabilities effectively. Information sharing is always a great way to minimize the risks brought about by both deliberate attacks and unintended failures.

Incident response preparedness: The framework also outlines how AI developers and operators should prepare for potential incidents; clear protocols must be in place to quickly address issues before they escalate.

What are the responsibilities of AI developers?

One of the most notable aspects of the DHS report is the explicit focus on the responsibilities of AI developers.

The guidelines set a new precedent by outlining clear expectations, especially for those creating AI tools meant to operate in or interact with critical infrastructure.

This focus on developers is particularly important because they are at the forefront of creating technology that directly influences critical systems. The decisions made during the design, development and deployment phases can have significant consequences and impact everything from public safety to national security. By giving developers a structured set of responsibilities, DHS is hoping to create a culture of accountability and foresight in the AI community.

As such, AI developers are encouraged to take the following actions to align with the new guidelines.

Design with risk in mind: Developers are urged to build AI systems that prioritize safety and resilience from the ground up, especially when the technology is intended to interact with critical services like power grids or communication networks. This means integrating fail-safes, conducting stress tests and simulating potential failure scenarios during the design phase.

Adopt explainable AI practices: Transparency is crucial for AI developers. The framework urges the adoption of explainable AI techniques that allow human operators to understand why certain decisions were made. This practice boosts trust while also providing an audit trail that can be useful in identifying the root causes of any issues that arise.

Collaborate for broader impact: Developers should not just work alone but actively engage with a broader community of stakeholders, including policymakers, users and other tech creators. After all, collaboration helps ensure that AI tools are safe, reliable and ready to operate under real-world conditions.

By following these guidelines, developers can help build AI systems that meet technical standards and also align with societal values and safety requirements. The focus on explainable AI, risk-based design and collaboration creates a balanced approach that can maximize the benefits of AI and minimize its potential downsides.

Why does this matter now?

The release of the AI framework is a good reminder that AI technology is not evolving in a vacuum. Today, AI is more pervasive than ever before, but its use in critical infrastructure demands the highest level of care and responsibility. With the focus on developers as important players in minimizing risks, the DHS is creating an environment where AI can thrive without compromising essential public services.

It’s important to note that the responsibility for secure AI extends beyond the developer stage. Tech organizations will play a key role as well. Arvind Krishna, Chairman and CEO of IBM, says, “The DHS Roles and Responsibilities Framework for Artificial Intelligence in Critical Infrastructure is a powerful tool to help guide the responsible deployment of AI across America’s critical infrastructure, and IBM is proud to support its development. We look forward to continuing to work with the Department to promote shared and individual responsibilities in the advancement of trusted AI systems.”

Secretary Mayorkas echoes those sentiments, adding, “The choices organizations and individuals involved in creating AI make today will determine the impact this technology will have in our critical infrastructure tomorrow.”

The secretary’s words capture the essence of why this framework matters: We need to shape the future of AI in a way that protects and enhances the services that are foundational to our society.

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