How To Design and Implement an Effective Incident Response Strategy for Businesses
Respond quickly to incidents and reduce operational impact.
An efficient response strategy is crucial to ensuring that businesses can quickly and effectively respond to incidents and reduce loss in revenue, data, customer trust, or compliance. In this blog, we'll go through the key steps in designing and implementing an incident management strategy covering the four steps of incident response as defined by NIST, namely: preparation, detection & analysis, containment & recovery, and correction of errors & continuous improvement. Lastly, we will discuss the importance of management buy-in and explore the role of automation in helping organizations improve their response capabilities.
The first step in building an incident response strategy is preparation. This involves working backwards from business goals to define Service Level Objectives (SLO), identify Service Level Indicators (SLI), and build Service Level Agreements (SLA) with the business teams. Service-level objectives (SLOs) are quantitative measurements of the performance of a service. SLOs specify the level of service that customers can expect from a system and are defined in terms of key performance indicators (KPIs), such as availability, response time, error rate, and throughput.
Service-level indicators (SLIs) are metrics that are used to monitor the performance of a service. SLIs are used to track the progress of a service towards meeting its SLOs. SLIs are typically measured on a regular basis, such as hourly or daily.
Service-level agreements (SLAs) are contracts between a service provider (internal or external teams) and a customer (internal or external) that specify the level of service that the provider will deliver to the customer. SLAs are typically negotiated between the parties and include penalties for the provider if they fail to meet their obligations.
All three of these terms are important for building incident response plans because they provide a framework for measuring and managing the performance of a service. Once this understanding has been built, the next step is to define the classification criteria for incidents by severity or priority based on their impact to the business. This classification forms the basis of the escalation process and the mode of response. The escalation plan should include the contact details of relevant teams, modes of communication, expected time to acknowledge or respond, and the chain of command for unresponsive teams.
For prescriptive guidance on this topic, refer here.
At this stage, it is important to ensure that the incident classification is shared and agreed upon by the cross-functional teams involved in the response. For example, for high severity incidents involving a public-facing impact, businesses can sometimes involve the marketing, social media, and legal teams before sharing details of the service impairment externally. However, if these teams do not work 24/7 or have a defined on-call register, it can lead to delays in external communications, leading to negative impact on the end customer experience. An effective incident response preempts this by defining these escalation paths in advance and having them documented and accessible to teams leading the incident response.
Important Key Performance Indicators (KPIs) for evaluating the effectiveness of the preparation step include: mean time to acknowledge (MTTA), mean time to escalate (MTTE), and incident distribution by severity over a period of time.
Detection and analysis, sometimes known as observability, is the ability to determine the current state of a system by collecting information from multiple sources. There are three primary types of observability: monitoring, diagnostics, and tracing. Monitoring involves collecting basic information about the system, such as CPU and memory usage. Diagnostics collect more detailed information about the system, such as log files and error messages. Tracing follows the path of a request through a distributed system, which can help identify performance issues and bottlenecks. Observability tools such as dashboards, alerts, and log management systems can help organizations gain insight into their systems and identify issues before they become problems. These systems add a layer of intelligence by correlating errors, logs, traces, CPU, and memory usage to identify potential issues and file incidents according to criteria defined during preparation.
A key goal of observability is detecting errors or failures before they manifest as customer impacts. One of the ways of doing this is through the use of synthetic monitoring. Synthetic monitoring involves the creation and deployment of synthetic transactions and monitoring these transactions from the perspective of the end user to ensure that they can be used to gain insights about performance and the user experience. It can be used for a comprehensive understanding of how well an application or system is performing and to identify and resolve performance issues before they impact end users. It can also be used to simulate load tests and perform capacity planning to ensure that an application or system can handle expected levels of traffic.
An important aspect of detection and analysis is understanding gray failures. Gray failures are a type of failure that is not immediately detected or flagged as an error in an IT system. Gray failures can cause intermittent issues that can be difficult to diagnose and troubleshoot, leading to delayed resolution and potential impact on business operations. These failures can also go unnoticed for long periods, leading to unexpected downtime and data loss. Detecting gray failures requires building differential observability which enhance on the underlying systems observability to also equip the consumers of your systems to both quickly detect and mitigate the impact of a gray failure.
Important Key Performance Indicators (KPIs) for evaluating the effectiveness of the detection and analysis step include: mean time to detect (MTTD) and percentage of overall incidents detected through customer cases.
For details on how to implement Observability for your AWS workloads please visit the AWS Well Architected Observability doc.
Containment and recovery focuses on the ability of the operations teams to use the tools and dashboards available to detect and then isolate failures from spreading across the system. Effective incident response plans ensure that teams who can drive mitigation and resolution get involved in the shortest amount of time from the start of the disruption. In order for engineering teams to focus on mitigation and resolution, the incident response plan can include dedicated incident managers who handle the communication and escalation process with the larger crossfunctional teams. Incident Managers can also gain approvals in case mitigation or resolution involves hot fixes or deployment rollbacks.
Containment and recovery can be further enhanced through pre-emptive steps like architectural patterns that focus on static stability or using fractional deployment strategies to reduce the blast radius of disruptive changes. While these can some times be beyond the scope of incident response teams, they have a positive impact on mitigation and resolution times.
For more details on static stability, control planes, and data planes, refer to the Amazon Builders’ Library article Static Stability using Availability Zones.
Important Key Performance Indicators (KPIs) for evaluating containment and recovery strategies include: mean time to mitigate, mean time to resolve, and availability measured as uptime, durability, or percentage of successful transactions.
Correction of errors (CoEs) or Root Cause Analysis refers to the process of objective deep dives into customer-impacting events after mitigation or resolution. CoEs are effective when they are performed with a learning mindset, are blameless, and focus on architecture, tooling, and process improvements - and not on the operators themselves. CoEs lead to improvements to testing procedures, design, architecture, or deployment strategies.
Continuous improvement of incident management practices can involve weekly operational reviews, experienced operator reviews of procedures, communications plans and incident severity definitions, and planned chaos engineering experiments in lower or production environments.
For details on how to build an effective CoE process, please refer to this blog.
Important Key Performance Indicators (KPIs) for evaluating the effectiveness of CoEs and continuous improvement processes include: mean time between failures (MTBF), reduction in operator errors, and number & percentage of improvement tasks completed per plan.
Management buy-in is the backbone of high-performing incident response teams and is required for each of the four steps outlined above to be effective. During the preparation step, senior leadership involvement can ensure crossfunctional alignment, approve escalation plans, and resolve conflicts in support structures. Senior leaders can foster a culture of ownership and autonomy which can reduce mitigation times by involving the responsible teams early in the incident life cycle. It plays an important role in ensuring CoE meetings stay blameless and the action items are prioritized and resolved by the responsible teams.
Automation is a key component of effective incident management. It can be used to automate incident reporting, tracking, and resolution. Automation can be used in communication & collaboration through alerts and notifications triggered in response to incidents. This can help to reduce the time and effort required to manually generate and distribute incident reports. Incident tracking can also be automated to automatically update incident statuses. It can also be used for assigning resources to incidents, initiating automated workflows, and triggering notifications to stakeholders.
For a deep dive on automating incident responses refer to AWS Prescriptive Guidance.
By taking a proactive approach to incident management, businesses can minimize the impact of security incidents and ensure that they are prepared to handle any situation that may arise. Businesses that are successful learn from each incident to drive positive changes and build a culture of ownership. As Amazon’s CTO Werner Vogels says, “Everything fails all the time”; organizations that practice their failure response are better equipped than the ones that are surprised by it. Learn more about topics covered in this blog by visiting the following links: Observability Best Practices, Control Planes and Data Planes, and Key Performance Indicators.