What is application risk management (ARM)?
Application risk management (ARM) is a systematic approach to detecting, assessing, prioritizing, and addressing risks in cloud-native software applications throughout the software development life cycle (SDLC).
Organizations deploy diverse software and software development tools in their IT stacks, ranging from Application Programming Interfaces (APIs) to libraries, frameworks, plugins, widgets, SaaS tools, and more. As each of these software components is added to the mix, application risk indices increase and attack surfaces broaden.
So how do you use these vital components while abstracting their risks? That’s where application risk management comes in. ARM is all about determining what risks software components have and mitigating them early. The end result? Strong application security, improved app performance, and adherence to compliance requirements.
In this article, we’ll unpack why and how ARM is done, common risks to look out for, and best practices for an effective ARM program.
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Why do enterprises need ARM throughout the SDLC?
With cloud-native architectures and AI now taking center stage at every phase of the SDLC, ARM has become a top priority. Both technologies—cloud and AI—have inherent risks that software applications built on or with them automatically inherit.
In the cloud, the build, deployment, and runtime phases all have their risks, including vulnerable images, misconfigured access, and privilege escalation. And with AI added at any phase of the software development process, the security risks double: AI can generate insecure code, embed vulnerable logic, or inadvertently expose sensitive data—especially when outputs are used without security review.
That’s why ARM must be implemented across every stage of the software development life cycle so that businesses can catch and fix risks early, before they balloon into breaches or performance degradation.
Let’s look at some real-life examples:
Phase 1: Software design
Case study: You’re building a fintech app that’ll potentially handle customer PIFI (personally identifiable financial information). But because you’re a small business, you’ve chosen a monolithic design.
ARM input: ARM tools flag potential problems a monolithic architecture could introduce, including a larger attack surface and potential problems isolating software components or fixing vulnerabilities.
Result: To mitigate risks you switch from this vulnerable-by-design architecture to a secure-by-design one.
Phase 2: Software development
Case study: You’re developing an e-commerce website with a payment gateway, but your AI coding tool has accidentally integrated an outdated API library with an improper input validation vulnerability. Left undiscovered, it could create backdoors for security exploits like SQL injection and XSS.
ARM input: Run automated SAST and SCA scans on all new software before deployment.
Result: The scans reveal the risk and you integrate a vulnerability management solution to auto-remediate.
Phase 3: Testing
Case study: Your dev team has hardcoded key infrastructure secrets to speed up testing, and they’re now committing the code (credentials included) to a code repo.
ARM input: Integrate a pre-commit hook that instantly detects and flags the exposed secrets for immediate resolution.
Result: If it’s a source code repo, you can block the commit; if it’s a private, low-risk code repo, you can choose to override the alert and subsequently rotate the secret.
Phase 4: Deployment
Case study: You’re deploying a container app on AWS ECS and you’ve attached an EBS volume containing sensitive PHI. Problem is, your identity and access management (IAM) admin has inadvertently defined some overprivileged roles, increasing the risks of lateral movement to sensitive data.
ARM input: Integrate an IAM scanner like AWS Access Analyzer, an IaC scanner, or a CIEM tool that auto-discovers access and resource-sharing risks.
Result: The tool auto-remediates the misconfiguration (for example, through a pull request comment), ensuring it doesn’t get to runtime.
Phase 5: Runtime
Case study: Your container’s running on a host with a kernel vulnerability, which could potentially allow attackers root access and control of your systems.
ARM input: Real-time runtime security tools, such as eBPF-based scanners or kernel-integrated detection solutions, flag the vulnerability before it’s exploited.
Result: You continue to use containers because they’re crucial to your IT stack, but you harden the kernel to minimize the risk of kernel exploits.
Implementing ARM across the SDLC is all about detecting risks promptly, quantifying the risks to determine if they should be fixed or accepted, and applying the necessary controls before moving to the next phase.
Beyond the obvious benefits, proactive ARM also ensures operational resilience and demonstrates to customers and regulatory bodies that your enterprise is committed to data security.
Your first step toward a smart ARM plan? An application risk assessment.
How do application risk assessments work?
Application security risk assessments are the process of identifying, quantifying, and fixing software risks. Here's a step-by-step breakdown of how application risk assessments work, including how to identify and prioritize risks.
Step 1: System characterization
Begin by getting a full picture of your software environment. Inventory all assets, including apps, code, dependencies, and containers so that you know what assets you have. Run vulnerability scans to uncover common risks posed by each.
Common types of risks and app vulnerabilities to look out for during risk assessment include code vulnerabilities (think poor input validation and cryptographic failures), cloud-native risks (e.g., misconfigured storage and weak access controls), runtime risks, compliance violations, and third-party dependency vulnerabilities.
Step 2: Threat modeling
Create diagrams mapping data flow across all assets to understand how various systems interact and possible exploits / exploit paths that could emanate from these interactions.
Use threat modeling frameworks like MITRE ATT&CK, STRIDE, and PASTA to understand how vulnerabilities, attack paths, and attacker TTPs (tactics, techniques, and procedures) relate.
Threat modeling is also useful for visualizing how systems can be isolated in case of an attack and determining specific security measures for blocking attack paths.
Step 3: Risk scoring
Prioritize risks by exploitability, likely business impact, and asset criticality. Use frameworks like EPSS and CVSS to determine which risks are most likely to get exploited and which would be most damaging if exploited.
Assign metric values to each risk, then prioritize remediation efforts accordingly. For example, the IAM risk we discussed above in the fourth case study has an attack path to sensitive data, meaning it’s high risk.
Step 4: Risk mitigation
Based on your risk scoring and risk appetite, decide which risks to accept (e.g., low-severity risks with minimal business impact and low compliance risk), which to transfer (for example, ransomware risks to cyber insurance), and which to resolve (think leaked secrets, outdated software dependencies, etc.).
Step 5: Compliance checks
Scan all assets for compliance violations and fix issues. It’s best practice to use tools that automate compliance management.
Step 6: Reporting
Document risks and mitigation steps to ensure relevant stakeholders are fully aware of the steps taken (and possible consequences). This information can guide security architects’ and incident responders’ future risk mitigation / incident response (IR) efforts.
Step 7: Continuous monitoring
Automate ongoing ARM to validate mitigation and keep risk management going. Discover and fix new risks across the SDLC.
Secure Coding Best Practices [Cheat Sheet]
In this 11 page cheat sheet we'll cover 10+ essential security topics, offering practical steps for areas like API security, input validation, and containerized application protection—ideal for both beginner and advanced users.
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Application risk management best practices
Turbocharge your ARM program by following these best practices:
1. Understand cloud and AI risks:
Cloud risks, like poor data isolation, access controls, and misconfigurations, put enterprises at risk of cyberattacks.
AI risks, such as model poisoning and data leakage, can compromise your systems’ performance and your organization’s integrity.
Shared cloud and AI risks like supply chain vulnerabilities and compliance violations widen the attack surface.
Figuring out how these risks can impact your applications and your bottom line can tell you what steps to take to manage risk and what risks to prioritize.
2. Apply the following application security best practices:
Apply fine-grained IAM via access control mechanisms like role-based access control (RBAC) and attribute-based access control (ABAC).
Adopt the principle of least privilege (POLP) to minimize lateral movement and insider attacks.
Enforce zero-trust to lock unauthorized entities out of your IT environment.
Encrypt data in transit and at rest to prevent data manipulation or theft.
Avoid hardcoding secrets. Instead, use environment variables, store secrets in vaults, and deploy secrets management tools.
Understand the shared responsibility model and meet your obligations to protect your assets.
Remember: Application security is one of the top goals of ARM!
3. Shift left, shield right:
Secure your IDE and CI pipelines with tools like IaC scanners and SCA solutions to detect and manage open-source dependency vulnerabilities, license risks, and outdated libraries.
Even when you’ve proactively implemented shift-left testing and risk mitigation, new vulnerabilities still get discovered post-deployment. That’s why it’s critical to continuously monitor your runtime environment and apply user behavior analytics and threat detection solutions.
4. Use the right threat models:
MITRE ATT&CK can help you understand how attacker TTPs relate to vulnerabilities in your environment.
STRIDE helps assess and mitigate software design flaws.
PASTA is great for quantifying the potential business impacts of application risks.
5. Manage dependency and supply-chain risks:
Have up-to-date software bills of materials (SBOMs) so that you understand the third-party risks you’re dealing with.
Enforce secure software procurement.
Avoid deploying vulnerable container images.
Keep software up-to-date with automated patching.
6. Prioritize risks because resources are limited, and not all risks are exploitable:
Use security solutions that sync multiple risk prioritization frameworks with nuanced considerations like data sensitivity, prevalent attacks targeting your industry, and compliance needs.
Promptly remediate risks in order of priority.
7. Deploy application security controls:
Implement strict authentication and authorization via multi-factor authentication (MFA) and RBAC.
Ensure proper input validation to block injection attacks.
Put logging, monitoring, and audit processes in place to detect and stop suspicious activities quickly. Use security information and event management (SIEM) systems.
Enforce continuous application security posture management (ASPM) to reduce your attack surface. ASPM is an emerging discipline that unifies and orchestrates risk signals across the software supply chain—covering code, pipelines, infrastructure, and runtime environments.
8. Develop a risk appetite framework and align your ARM strategy with it:
Document your organization’s stance towards various types of cloud-native application risks.
Create IR playbooks for handling security incidents smoothly.
Define your risk tolerance levels with clear key risk indicators (KRIs) and incident recovery thresholds.
9. Adopt application security frameworks:
Consider implementing the NIST Risk Management Framework (RMF), ISO/IEC 27001, and SOC 2.
Double-check that your risk management plan follows industry standards.
Compare your risk appetite framework with the compliance standards for your industry.
10. Get stakeholder support on risk mitigation steps:
Ask stakeholders to weigh in on aligning ARM with overall enterprise goals.
Carefully document risks and mitigation processes to ensure all stakeholders are aware of risk management decisions and likely impacts.
Continuously assess your stack for emerging risks.
Wiz for application risk management
Wiz Code brings context and automation to every phase of application risk management, helping you secure your cloud-native software development lifecycle from end to end. With Wiz Code, you can:
Inventory and scan assets early in the SDLC, including source code, IaC, and container images.
Map data flow and detect toxic combinations through Wiz’s Security Graph, which acts like a threat modeling engine—connecting vulnerabilities, identities, and exposed data.
Prioritize application risks based on exploitability, business context, and proximity to sensitive data—similar to how risk scoring works in EPSS/CVSS.
Fix issues directly at the source, whether in the IDE, CI/CD pipeline, or GitHub PR, with inline remediation guidance and auto-fix capabilities.
Maintain runtime coverage with real-time visibility into misconfigurations, kernel vulnerabilities, exposed secrets, and drift from secure configurations.
Automate compliance checks and risk reporting for stakeholders and auditors with enriched cloud context and evidence gathering.
By mapping application risks from code to cloud and enriching every finding with deep context, Wiz reduces alert fatigue, accelerates remediation, and empowers both development and security teams to act confidently.
Want to see how it works? Request a personalized demo and experience how Wiz helps operationalize application risk management in your environment.
