Cracking the Code: A Comprehensive Guide to Secrets Detection

Secrets detection is essential, as they (secrets) are the key to controlling access to confidential systems, authenticating resources, and building secure applications. They’re also an attractive target for malicious actors who seek to exploit them. We’ve seen this in recent breaches that affected Microsoft and Uber. 

The consequences of secrets leaked by threat actors are far-reaching, costing organizations $1.2 million in revenue on average, according to research by Password1. Management and detection are top priorities for security teams and developers. But it’s a tightrope to walk: organizations must leverage what is essential, while simultaneously mitigating potential risks. 

To mitigate these risks, organizations have started to prioritize DevSecOps and continuous integration, necessitating the implementation of robust practices that minimize the risk of secret exposure. This includes encryption, access controls that follow the principle of least privilege, secure storage practices, rotation policies, and multi-factor authentication.

But this doesn’t account for human error, supply chain risks, or incorrectly implemented encryption. That’s why security teams leverage secrets detection.

In this article, we explore secret exposure in detail, including challenges, best practices for effective management, and tools that’ll help you keep your most sensitive data safe. 

Key takeaways:

• Secrets detection is the essential process of automatically finding sensitive credentials, such as API keys, passwords, and encryption keys, which, if exposed, can lead to devastating data breaches and compliance violations.
• Effective detection is challenging due to the high volume of false positives, limited coverage from point solutions, and the operational complexity of managing numerous security tools across the rapid CI/CD pipeline.
• Best practices require a “shift-left” strategy, including implementing pre-commit hooks, integrating continuous secret detection throughout the SDLC, and adopting robust secret rotation policies and developer education.
• The most comprehensive approach is adopting an AI-Native Application Security Platform like Cycode, which unifies secret scanning with contextual risk prioritization and automated remediation across the entire Code-to-Cloud lifecycle.

What Is a Secret in Cybersecurity?

In the digital world, secrets are sensitive pieces of information that organizations don’t want publicly exposed. If they were in a physical folder, it’d be marked ‘CONFIDENTIAL’ and locked away in a filing cabinet. While the vast majority are authentication-related, that’s not a qualifying characteristic.

Here are just a few examples of secrets:

• Usernames and passwords
• Encryption keys
• API keys
• Tokens and session IDs
• Private keys
• Digital certificates
• Biometric data
• Configuration files
• Personally identifiable information (PII)

Regardless of the type of secret, they’re intended to be protected from unauthorized access because they pose an incredible security risk.

Why Leaked Secrets Are a Security Risk

A compromised Netflix password doesn’t pose the same security risk as an encryption key for a database when it comes to source code secrets detection. One might expose embarrassing viewing history, while the other could expose customer data.

In the case of the latter, secrets can be used to gain unauthorized access to systems, applications, and sensitive data. Lateral movement exacerbates existing attacks and, because authentic credentials are used, they’re harder to detect, take longer to contain, and the consequences are greater. This includes Separation of Duty (SoD) violations.

Of course, secrets only become dangerous when they’re exposed to attackers. So, how does that happen? Leaks generally happen in one of three ways: 

1. Compromised Accounts: Whether via phishing, brute force attacks, or another tactic, bad actors can compromise an account, move laterally throughout the SDLC, and gain access to code that has secrets in it. While compromising accounts isn’t a new exploit, developers have become even higher-value targets due to the level of access their accounts now provide, particularly when it comes to code repositories, logs, and configuration files.
2. Malicious Insiders: Someone with legitimate access to a system could abuse their privileges to alter code, change access controls, or obtain and leak sensitive information.
3. Source Code Leak: It’s surprisingly easy to leak code accidentally. Code could be committed to the wrong repo, or a misconfiguration could lead to faulty access controls. Either way, if it ends up in the public domain and it has secrets in it, attackers can worm their way into protected systems.

Dangers of Secret Exposure

The true cost of secret exposure extends far beyond immediate remediation; it represents a fundamental compromise of trust and security throughout the software supply chain. When credentials—the digital keys to your most sensitive assets, fall into the wrong hands, the resulting fallout impacts everything from immediate financial stability to long-term regulatory standing. This is why effective secrets detection is not merely a security checklist item but a critical business imperative that shields the organization from five major categories of catastrophic harm.

Data Breaches 

When attackers obtain and exploit secrets, they can potentially gain access to a vast amount of sensitive information, including personal data, proprietary source code, and trade secrets. Because a secret grants legitimate access, the attacker can often bypass perimeter defenses unnoticed, allowing them to remain in the network for extended periods before exfiltrating massive volumes of data.

As we’ve learned from high-profile data breaches, the consequences are severe, including financial loss, reputational damage, and the irreversible loss of customer trust. Furthermore, the exposure of intellectual property like source code can permanently diminish a company’s competitive edge in the marketplace, fundamentally threatening its long-term viability.

Financial Loss 

In addition to the financial loss caused by a data breach (which has climbed to $4.45 million on average according to IBM’s annual Cost of a Data Breach report), compromised secrets can directly lead to fraudulent activities. For example, leaked bank credentials, payment gateway keys, or cloud infrastructure billing secrets can be used to conduct unauthorized transactions or provision massive, malicious compute resources.

The costs associated with a secret breach are multifaceted: they include incident response, forensic investigations, legal fees, public relations management, and the potential for increased insurance premiums. By detecting and remediating secrets early, organizations can drastically limit the financial blast radius and avoid these crippling secondary costs.

Unauthorized Operations

Secrets grant privileged access, enabling attackers to perform actions far beyond simple data theft. Attackers with access to deployment tokens or infrastructure credentials may manipulate data, escalate their privileges, or actively disrupt mission-critical services and applications.

The most insidious form of this danger is when an attacker compromises the software a vendor provides to customers, a supply chain attack that affects an entire ecosystem. As we saw in the infamous SolarWinds attack, this can have cascading consequences across critical infrastructure or financial systems. Preventing unauthorized operations requires unified visibility to track a secret’s potential impact on core business functions.

Account Hijacking 

Attackers may use stolen credentials to hijack legitimate user or service accounts. This is a common consequence of secrets leaked in collaboration tools or development environments, granting access to internal accounts, customer-facing applications, or cloud dashboards.

Account hijacking can lead to unauthorized access, manipulation, and even permanent deletion of user data or organizational assets. Because the attacker is using valid credentials, the activity often bypasses traditional anomaly detection systems, making early secret detection and rotation the most effective prevention against this threat.

Compliance Violations

Beyond the technical risks, compromised secrets can result in severe legal and regulatory penalties. Regulations like GDPR, HIPAA, and PCI-DSS mandate strict procedures for protecting sensitive information, including encryption keys and authentication credentials.

Implementing robust secret detection demonstrates proactive compliance by continuously monitoring the codebase and environment for violations. Failure to secure these credentials can lead to substantial fines, mandatory reporting requirements, and costly legal consequences, highlighting the regulatory necessity of a strong secrets management program.

What Is Secret Detection?

Secrets detection is an automated process that scans code and configurations for patterns and formats indicative of sensitive information. To ensure comprehensive coverage, detection should be integrated into continuous integration and continuous deployment (CI/CD) pipelines, and these should be scanned:

• Source Management Control (SCM) tools
• Version histories
• Public and private repositories
• Delivery pipelines
• Kubernetes resources
• Containers
• Productivity tools like Slack and Confluence

The goal is simple: detecting secrets in source code early and mitigating leaks. Importantly, strong secrets security doesn’t eliminate the need for robust Sec and Dev practices. It’s a vital additional layer of defense.

How Do Secret Detection Tools Work?

Secret detection tools employ a multi-layered approach to sniff out hidden secrets lurking in your code. 

• Pattern Matching: Algorithms can be used to search for known patterns or formats of sensitive information. For example, they might look for strings that match the typical structure of API keys, passwords, or other credentials.
• Regular Expressions: These can also be used to define patterns that match specific types of secrets, allowing for flexible and customizable search criteria.
• Entropy Analysis: Some advanced secret detection tools use entropy analysis to identify sequences of characters that exhibit characteristics of random or encrypted data. This can help uncover potential secrets, even if they don’t follow standard patterns.
• Dictionary-Based Detection: Dictionaries or predefined lists of known sensitive terms or keywords can also be used to help identify common names or phrases associated with secrets.
• Artificial Intelligence (AI) and Machine Learning (ML): More recently, AI and ML have been used to automate and improve the accuracy of secret detectors. Models can be trained on large datasets to recognize patterns, sequences, and anomalies associated with sensitive information.

When potential secret exposure is detected, these tools typically generate alerts or reports to inform developers or security teams. Once identified, organizations can take steps to secure or rotate the leaked secrets and improve their overall security posture.

How to Evaluate Leading Secrets Detection Solutions

The process of selecting the ideal tool for detecting secrets in source code and across the broader Software Development Lifecycle (SDLC) is a complex undertaking that extends beyond feature checklists. Since a single exposed secret can be catastrophic, the chosen solution must deliver more than just high detection rates; it must offer true enterprise manageability, developer alignment, and contextual intelligence. To avoid costly tool sprawl and alert fatigue, organizations must evaluate vendors based on their ability to deliver comprehensive coverage, seamless integration, and operational efficiency across these five critical areas.

Assess Coverage and Accuracy Across Environments

The most significant differentiator among secret detection solutions is the scope of coverage and the accuracy of their detection engine. Modern secrets sprawl across source code, CI/CD pipelines, IaC files, and non-code assets like collaboration platforms (Jira, Slack). A point tool limited to only Git repositories leaves massive, exploitable blind spots in your Software Supply Chain.

Furthermore, relying on simple regex-based detection leads to an unmanageable volume of false positives, which quickly erodes trust between security and development teams. Effective solutions must use advanced, proprietary methods like machine learning and entropy analysis to achieve high-fidelity results. This ensures your team focuses only on actively exploitable secrets, minimizing wasted time and maximizing risk reduction.

• Breadth of Coverage: Verify the tool scans source code, full commit history, CI/CD logs, cloud configurations (IaC), and collaboration tools.
• Detection Methodology: Look for context-aware, machine learning (ML) or AI-powered engines that prioritize accuracy over simple pattern matching.
• Customization: Ensure the tool allows for the creation of organization-specific secret patterns to cover proprietary tokens or credentials.

Check Integration Capabilities

A secret detection solution that does not seamlessly integrate into existing developer and security workflows will inevitably be bypassed or ignored. The goal of DevSecOps is to embed security without adding friction, and this relies entirely on the tool’s ability to communicate with your current stack, including your Version Control System (VCS), CI/CD runner, and IDE.

Seamless integration facilitates a true “Shift-Left” approach, enabling prevention mechanisms like pre-commit hooks and real-time scanning during the Pull Request (PR) process. A platform-based solution, like Cycode’s ASPM, connects all these disparate tools to provide a unified risk view, eliminating the need to manage multiple dashboards and data silos.

• VCS Integration: Confirm native, deep integration with all your Git platforms (GitHub, GitLab, Bitbucket) and their underlying APIs.
• CI/CD Pipeline Compatibility: The tool must integrate as a mandatory, blocking step within your CI/CD pipelines (e.g., Jenkins, GitHub Actions) to prevent secrets from being deployed.
• Developer Workflow: Look for IDE extensions and automatic ticketing/alerting within native tools (e.g., Jira, Slack) to deliver guidance where developers work.

Consider Ease of Use and Developer Adoption

For any security tool to succeed in a modern enterprise, it must achieve high adoption among the engineering teams. A tool that is difficult to set up, requires complex configuration, or generates alerts without context creates immediate friction between security and developers. The chosen solution must be developer-friendly, offering actionable guidance rather than simply throwing cryptic errors.

Solutions designed with the developer in mind provide clear, in-workflow remediation steps, documentation, and rationale for the finding, fostering a collaborative security culture. An easy-to-use platform reduces the learning curve for security and development teams alike, accelerating Mean Time to Remediation (MTTR) and minimizing operational overhead.

• Time-to-Value: Evaluate how quickly the solution can be deployed and deliver the first set of actionable results.
• Remediation Guidance: The tool must provide specific, easy-to-follow steps on how to fix the exposed secret and prevent future occurrences.
• User Interface (UI): Assess whether the platform’s dashboard is intuitive for both security analysts prioritizing risk and developers focusing on fixing their assigned tasks.

Review Alert Management and False Positive Handling

Alert fatigue is the single greatest threat to an organization’s security posture, leading teams to tune out or ignore critical warnings. Traditional scanners often flood security teams with thousands of low-priority or false-positive alerts. The ideal solution must incorporate intelligent prioritization to filter the noise and surface the handful of findings that pose an existential risk.

Advanced platforms use risk scoring (such as Cycode’s Risk Intelligence Graph) to correlate a secret’s exposure with its potential exploitability—is the secret active? What production system does it access? Is the repository public? By providing this contextual risk, the tool helps security and development teams efficiently focus their limited resources on the issues that truly matter.

• Contextual Prioritization: The tool should prioritize based on exploitability, impact (e.g., production vs. test), and data sensitivity, not just the existence of a secret.
• False Positive Reduction: Ask for statistics on the tool’s false-positive rate and how its detection engine minimizes noise.
• Suppression/Baselining: Verify the ability to suppress known, non-sensitive findings and manage security debt without cluttering the active alert dashboard.

Analyze Scalability, Support, and Total Cost of Ownership

Enterprise environments are characterized by massive, complex repositories and thousands of commits daily. The chosen secret detection solution must be able to scale efficiently without becoming a bottleneck in the CI/CD pipeline or incurring prohibitive costs. Scalability relates not just to volume but also to supporting multi-cloud and multi-VCS environments.

Finally, while open-source tools appear free, their Total Cost of Ownership (TCO) often balloons due to time spent on maintenance, managing false positives, and developing custom integration scripts. Commercial vendors offer dedicated support, continuous updates, and often a more cost-effective unified platform (ASPM) approach that consolidates spending on multiple point solutions.

• Scaling Performance: Confirm the tool can handle full historical scans of multi-gigabyte repositories and high-frequency commits without degrading performance.
• Support Model: Evaluate the availability, responsiveness, and technical depth of the vendor’s dedicated support team for critical issues.
• Pricing Structure: Understand if the cost is based on users, repositories, or commits, and calculate the true TCO versus managing multiple open-source and point solutions.

Challenges of Enterprise Secrets Management and Detection

Implementing an effective secrets management and detection program at the enterprise level is fraught with technical and operational difficulties that often derail security initiatives. Modern development complexity—driven by multi-cloud adoption, rapid CI/CD cycles, and an explosion in the number of security tools—creates significant friction points. These challenges span from the inherent inaccuracy of traditional detection methods and crippling alert fatigue to the systemic breakdown of collaboration between security and development teams, necessitating a platform approach that can provide unified context and streamlined workflows.

Limited Coverage

Regular expressions, dictionary-based detection, and pattern matching all rely on predefined lists, which may not encompass all possible variations and permutations of sensitive information, especially in dynamic environments where data changes frequently. Attackers can easily devise new terms or use obfuscation techniques to evade detection.

Tool Sprawl

The average organization uses 50+ security tools across its developer and security teams,  according to our 2025 State of ASPM report, often involving a mix of on-prem and cloud-based systems.

While these tools were implemented to help fortify defenses, the proliferation of AppSec tools is overwhelming the very people tasked with managing them. In fact, 4 out of 5 security professionals say they find managing multiple different security tools challenging.

Unsurprisingly, the more tools an organization uses, the more likely they are to be concerned about gaps in visibility.

Managing Secrets in a Multi-Cloud Environment

Some organizations rely on multiple cloud providers to avoid vendor lock-in, distribute workloads, and improve performance. 

But because different cloud providers may have their own native solutions for managing secrets security, it can be difficult to develop a unified approach. Similarly, ensuring consistent security practices across multiple clouds (especially with different security models and compliance requirements) can be complex and challenging, with a lot of operational overhead. 

Alert Fatigue and False Positives

Security detection generates alerts for potential security incidents or policy violations. Now, imagine the number of alerts teams are bombarded with when nearly 50+ tools are under their purview. So many alerts (many of which are likely false positives) can lead to alert fatigue. On a good day, this just makes it more challenging to identify and prioritize genuine security threats. 

On a bad day, it may cause security and developer teams to overlook or ignore alerts, potentially allowing real security incidents to go unnoticed. 

Integration with CI/CD Pipelines

CI/CD pipelines are designed for rapid and continuous software development, testing, and deployment. But security measures often introduce additional steps or checks that can slow down the pipeline, contradicting the goal of agility. The result? Developers bypass, neglect, or ignore them.

Collaboration Between Security and Dev Teams

Developers are under pressure to deliver innovative solutions, and fixing security issues isn’t their primary job. That means that, when security hands alerts to developers with little or no context, it stresses an already-strained relationship. 

It’s no wonder our research shows 74% of security professionals say the relationship between security and developers needs to improve. 

The secret (get it?) to a more collaborative and effective relationship? A detection and management tool that meets the unique requirements of both security teams and developers. That means it must provide clear visibility of all attack vectors, minimize alert fatigue, and seamlessly integrate into developer workflows.

How to Implement Source Code Secrets Detection

Successfully implementing a robust program for detecting secrets in source code requires a structured, multi-stage approach that moves from initial risk assessment to continuous, automated governance. This process ensures that security is baked into the development workflow—not bolted on later. By following these five essential steps, enterprises can transition from reactive vulnerability management to proactive risk prevention, securing the entire code lifecycle from commit to cloud.

1. Identify Where Secrets Might Exist

Before selecting a tool, you must first define the full scope of your risk, recognizing that secrets sprawl far beyond active code files. Secrets exposure can occur anywhere a developer interacts with credentials, including historical Git commit logs, legacy repositories, build system configurations, and internal communication tools. Failing to map this entire attack surface means your detection strategy will have critical blind spots.

To achieve comprehensive coverage, organizations need to perform a holistic audit of all source code management (SCM) systems, CI/CD environments, and cloud infrastructure-as-code (IaC) templates. This initial step is vital for informing the selection of a detection platform that can provide the necessary breadth to secure both current and historical data across every environment.

• Audit All SCM: Map and inventory every repository across all platforms (GitHub, GitLab, Bitbucket), including old branches and entire commit histories.
• Inventory Non-Code Assets: List all potential non-code leak vectors, such as ticketing systems (Jira), documentation, and build server logs.
• Define Sensitive Data: Establish a clear policy defining what constitutes a “secret” within your organization (e.g., proprietary tokens, encryption keys, PII).

2. Select a Secrets Detection Tool or Platform

Choosing the right solution is the most impactful technical decision in the implementation process. Enterprises should prioritize a comprehensive AI-Native Application Security platform, like Cycode, over standalone point tools. Application Security solutions provide a unified engine that can accurately detect diverse secret types using proprietary machine learning models, moving beyond the high false-positive rates of simple open-source regex scanners.

The selected platform must offer superior accuracy to minimize alert fatigue and must have the ability to scale seamlessly across large codebases and multi-cloud environments. Crucially, it should integrate deeply with all stages of your Software Development Lifecycle (SDLC) to ensure continuous monitoring and effective communication between security and engineering teams.

• Prioritize Accuracy: Select tools that employ advanced detection methods (ML, entropy analysis) to reduce false positives and find obfuscated secrets.
• Ensure Breadth: Confirm the platform’s detection capabilities extend beyond source code to cover IaC, build logs, and collaboration platforms.
• Evaluate TCO: Factor in the total cost of ownership, including maintenance, integration effort, and the value of consolidated reporting versus managing multiple point solutions.

3. Integrate Detection into CI/CD Pipelines

To truly “Shift-Left” security, secrets detection must be an automated, non-negotiable part of the continuous integration and continuous deployment (CI/CD) process. Integrating the scanner as an early gate ensures that secrets are caught and blocked before they can enter the main codebase, eliminating the need for costly remediation later.

Beyond simply running a scan, the integration must be lightweight and fast enough to avoid slowing down developer velocity, preventing teams from bypassing security checks. This includes implementing pre-commit hooks directly into the developer workflow and making the scanner a mandatory step for every Pull Request (PR) or merge event.

• Implement Pre-Commit Hooks: Enforce local checks on developer workstations to prevent accidental commits of credentials.
• Mandate PR/Merge Checks: Make secret scanning a mandatory, blocking step for all code merges to stop leaks before they hit the main branch.
• Scan Build Logs: Configure the tool to continuously monitor CI/CD build logs and environment variables where secrets often leak during the automation process.

4. Prioritize and Remediate Exposed Secrets

A high volume of alerts is useless without the context to prioritize them. Once secrets are detected, the implementation process must define how findings are ranked and actioned. This is where Application Security platform’s risk intelligence is invaluable, correlating the exposed secret with its potential impact (e.g., public repo, production access, active key).

Remediation must be swift, automated, and developer-friendly. The solution should facilitate automatic secret rotation or revocation and provide clear, in-workflow guidance to developers on how to fix the code and adopt secure practices. This automated remediation drastically reduces the Mean Time to Remediation (MTTR) and minimizes the window of exposure.

• Apply Contextual Risk Scoring: Utilize a risk graph to prioritize findings based on exploitability, impact, and location, focusing developer effort on critical issues.
• Automate Response: Implement workflows for automatic ticket creation, developer notification, and (where possible) secret rotation/revocation.
• Verify Fixes: Establish a process to automatically verify that the secret has been removed from the code and successfully rotated in the target service.

5. Establish Ongoing Governance and Monitoring

Secrets detection is not a one-time deployment but a continuous security practice. Effective implementation requires establishing clear governance, ownership, and metrics for long-term program success. Security and development teams must agree on policies, remediation SLAs, and reporting structures.

The detection platform should provide centralized dashboards and consistent reporting to monitor key performance indicators (KPIs) like MTTR for secrets, the number of successful blocks via pre-commit hooks, and overall secrets reduction over time. This continuous monitoring ensures that the security program remains effective, scales with the organization, and meets evolving compliance requirements.

• Define Clear Ownership: Establish who (Security, DevOps, or the specific development team) is responsible for triaging and remediating findings.
• Monitor KPIs: Track metrics such as the time to fix (MTTR), false-positive rates, and secrets density per repository.
• Regular Policy Review: Periodically review and update custom detectors and security policies to align with new cloud services, technologies, and compliance mandates.

Best Practices for Secret Detection within Your Organization

Detecting secrets within your codebase is essential, but it comes with its challenges — false positives, full coverage, and seamless integration with existing workflows. Here are the top best practices that can help you overcome these challenges and ensure sensitive information stays secure.

1. Implement Pre-Commit Hooks: One of the most effective tools for preventing secrets from entering your codebase is pre-commit hooks. By running automated scans on your code before it’s committed to version control, you can catch secrets early and reduce the chances of them ever reaching production. This proactive approach helps stop leaks before they start.
2. Integrate Secrets Scanning Throughout the SDLC: Secrets scanning should be an ongoing process, not a one-off. To ensure continuous protection, integrate secrets scanning into every phase of the SDLC—from development to production. By doing so, you catch secrets wherever they may appear, providing comprehensive coverage across environments and reducing the risk of exposed data.
3. Regular Secret Rotation Strategies: To minimize the impact of any exposed secrets, establish a routine for rotating API keys, passwords, and other sensitive data. Regular rotation ensures that even if secrets are exposed, they become obsolete quickly. This practice strengthens your security posture by limiting the potential window of exposure.
4. Developer Education and Training: One of the best ways to prevent secrets leaks is by empowering your developers. Provide regular training on secure coding practices, the risks of hardcoding secrets, and how to use secure storage solutions. An informed team is key to reducing human error and fostering a culture of security.
5. Take a Platform Approach vs. Point Solutions: Instead of relying on isolated tools for secrets detection, opt for a platform approach that integrates secrets scanning with other security functionalities. This unified approach offers better visibility, streamlines workflows, and ensures that secrets management aligns with your overall security strategy, providing comprehensive protection across your development lifecycle.

Fix Risk that Matters with Cycode’s AI-Native Application Security Platform

Cycode’s AI-Native Application Security provides visibility, prioritization, and remediation to help security, engineering, and DevOps teams detect and manage secrets. Here’s how:

Prevention

There are a few crucial steps organizations should take to prevent secrets from making their way into code in the first place. Developers should implement regular reviews, follow the principle of least privilege, and use encryption to ensure sensitive information remains secure when being transferred over networks. 

Secret management tools can also help, and with Cycode’s ASPM, you can bring multiple tools together in one view to ensure secrets are securely stored and managed. 

Visibility

The breadth and depth of Cycode’s end-to-end scanning capabilities enable security teams and developers to find API keys, tokens, proprietary information, and other secrets, even those that aren’t hidden in source code. This includes build logs, infrastructure, Kubernetes clusters, version history, and even collaboration tools like Slack and Confluence. 

Users are promptly notified of public source code exposure, and workflows can be used to send alerts, create tickets, and automatically resolve the leak of private resources.

Have unique requirements? Cycode supports custom secrets and policies, too.

Prioritization

Not all secrets are created equal. That’s why Cycode automatically helps teams prioritize remediation with customer risk scoring based on criticality, location, and likelihood of secret exposure. This ensures developer efforts are focused where they will be most impactful so they can reduce risk and get back to writing code. 

Remediation

Cycode’s developer-friendly workflows make it easier than ever to enforce coding best practices and stop secrets before they enter your codebase. The platform also offers developers the tools they need to detect secrets in code and fix leaks in their native environment. That’s right. Cycode isn’t just another tool developers have to adopt, with another dashboard to check. 

Want peace of mind that your data is safe? Book a demo now to see how Cycode can enhance your secrets detection capabilities.