How can unifying observability and security strengthen your business?

Bolster your organization’s observability and security capabilities on one platform with AI, anomaly detection, and enhanced attack discovery


Organizations in today’s digital landscape are increasingly concerned about service availability and safeguarding their software from malicious tampering and compromise. The traditional security and observability tools often operate in silos, leading to fragmented views and delayed responses to incidents. 

A unified platform integrating observability and security is essential for accelerating software delivery and performance and strengthening security. Leveraging AI and ML technologies, along with advanced attack discovery methods, can significantly improve this integration, providing a holistic and proactive approach to managing security and application health.

The challenge: Fragmented tools and data silos

Businesses today grapple with several pressing issues:

  • Tool sprawl: Proliferation of specialized tools makes data sharing complicated.

  • System/network centric approach: This hampers collaboration and data correlation.

  • Data duplication and retention: Redundant data and authority concerns slow down operations.

  • Proprietary software: Data often remains trapped within proprietary systems and multiple function-specific protocols.

These problems contribute to a fragmented view of the IT landscape, making it difficult for teams to respond to threats efficiently. According to Mimecast’s State of Email & Collaboration Security report, a staggering 74% of cyber breaches are caused by human factors, and 41% of organizations have experienced more email-based threats recently. This fragmented approach also poses a challenge to user management and system integration, as organizations struggle to adapt and grow.

The solution: A unified approach with AI and ML

Combining observability and security into a single platform offers numerous benefits, particularly when enhanced by AI and ML technologies:

  • Unified tools and centralized analysis: By integrating observability and security, teams can share insights and data, leading to more informed decisions.

  • Cost savings: Reducing the number of tools and platforms leads to direct cost savings and less administrative overhead.

  • Enhanced context and visibility: A unified approach allows for comprehensive monitoring across all data points, from logs and metrics to traces, events, and even business data.

  • AI-driven insights: Generative AI and ML provide advanced data analysis, identifying patterns and anomalies that human analysts might miss.

Elastic AI Assistant: The bridge for combining observability and security

Elastic AI Assistant is a key component in this integrated approach, offering faster detection and response times. Here’s how:

  • Generative AI: Elastic AI Assistant harnesses the power of generative AI to create tailored experiences for business, operations, and security teams. This interactive natural language chat interface allows users to zero-in on the most relevant information quickly — breaking down knowledge silos and improving response times.

  • Enhanced investigations: The assistant bolsters investigations by leveraging AI to provide contextual insights and actionable recommendations — enhancing the existing knowledge base of SRE and security teams.

  • Interactive chat interface: Built on the Elasticsearch Relevance Engine (ESRE) and powered by generative AI, Elastic AI Assistant integrates organization-specific knowledge with retrieval augmented generation (RAG) and transforms traditional search methods into an interactive and intuitive experience.

elastic ai assistant for observability

Accelerating problem resolution with AI and ML

AI and ML play a crucial role in accelerating problem resolution across observability and security functions.

elastic machine learning
  • Real-time KPIs: Provide real-time key performance indicators (KPIs) such as user experience, customer satisfaction, application performance, and system health metrics.

  • Anomaly detection: Advanced ML algorithms detect anomalies by establishing baseline behavior profiles and identifying deviations. This helps in spotting unusual patterns, potential issues, and operational inefficiencies.
  • Predictive insights: AI-driven insights enable predictive analytics, helping to foresee and mitigate potential disruptions before they escalate. Proactive alerts integrated with incident management systems enable teams to resolve potential issues before users become aware of them.

Attack Discovery: Enhancing security operations

Attack Discovery is a critical aspect of unifying observability and security. Elastic Security, powered by the Elastic Search AI Platform, introduces innovative features for advanced attack detection and response:

  • AI-Driven Security Analytics: Powered by the Elastic Search AI platform, Elastic Security is automating manual processes for configuration, investigation, and response. The platform combines search and retrieval augmented generation (RAG) to deliver highly relevant results. It integrates seamlessly with security workflows, providing prebuilt prompts and the ability to add custom prompts.

  • Elastic Attack Discovery: This feature triages hundreds of alerts to identify the few attacks that matter, providing an intuitive interface for security operations teams to understand and quickly act on attacks.
elastic ai assistant for security

By integrating observability and security functions and leveraging the power of AI and ML, businesses can streamline data collection, enhance threat detection, and optimize operational efficiency. This unified approach not only strengthens the security posture but also promotes a collaborative environment where data and insights are readily accessible to all relevant teams.

The importance of a unified approach

The complexity of software supply chains demands a unified approach to security and observability. Traditional application security measures are insufficient to address the intricate nature of the software supply chain. Elastic's adoption of the Supply Chain Levels for Software Artifacts (SLSA) framework enhances security by preventing tampering, improving integrity, and securing software packages and infrastructure. This proactive stance ensures that potential threats are mitigated and software supply chains remain secure.

Incorporating a unified observability and security platform enhanced by AI and ML is not just a technical upgrade — it's a strategic imperative. As cyber threats become more sophisticated and data environments more complex, the need for integrated solutions that offer both visibility and security becomes paramount. By adopting this approach, organizations can ensure a resilient, secure, and efficient digital landscape, ready to tackle the challenges of today and tomorrow.

Read the SANS report, Shining a light in the dark: Observability + security, or watch the webinar to learn more about this emerging strategy and how you can take steps to unify your organization’s observability and security functions.

The release and timing of any features or functionality described in this post remain at Elastic's sole discretion. Any features or functionality not currently available may not be delivered on time or at all.

In this blog post, we may have used or referred to third party generative AI tools, which are owned and operated by their respective owners. Elastic does not have any control over the third party tools and we have no responsibility or liability for their content, operation or use, nor for any loss or damage that may arise from your use of such tools. Please exercise caution when using AI tools with personal, sensitive or confidential information. Any data you submit may be used for AI training or other purposes. There is no guarantee that information you provide will be kept secure or confidential. You should familiarize yourself with the privacy practices and terms of use of any generative AI tools prior to use. 

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