Three years ago, I watched a production deployment grind to a halt because a penetration test — scheduled after code had already shipped — found a critical SQL injection flaw. The fix took forty minutes to write and four weeks to release through a frozen change management process. That gap, between writing vulnerable code and catching it, is exactly what DevSecOps is designed to close.
By 2026, DevSecOps has moved from buzzword to baseline expectation. Organizations that treat security as a pre-release gate are losing competitive velocity and, paradoxically, accumulating more risk. The teams that integrate security into every commit, every pipeline run, every container image push — those are the ones shipping faster and sleeping better at night. This guide covers everything I have learned building and scaling DevSecOps programs, from first principles to the specific tooling decisions that matter in 2026.
1. What DevSecOps Actually Means — And What It Doesn't
DevSecOps is the practice of embedding security controls, processes, and responsibilities directly into the software development lifecycle (SDLC) rather than appending them at the end. The term was coined to address a structural problem: DevOps accelerated delivery pipelines so much that traditional security review cycles became bottlenecks — and eventually bypass targets.
The definition sounds simple, but the implementation is frequently misunderstood. DevSecOps is not the same as:
- Running a SAST scan on a nightly build and calling it a pipeline
- Giving the security team a Jira board and declaring integration achieved
- Buying a platform that promises "DevSecOps in a box"
- Renaming your security team "DevSecOps team" without changing any process
Real DevSecOps shifts the unit of accountability. Developers own the security of the code they write. Platform engineers own the security of the infrastructure they provision. Security engineers become enablers, tool builders, and advisors rather than gatekeepers. This is culturally more challenging than any technical integration, and I will come back to it repeatedly throughout this article.
DevSecOps vs. DevOps: The Core Distinction
DevOps united development and operations around a shared goal: fast, reliable delivery. DevSecOps extends that union to include security as a first-class concern. In practice, this means security requirements enter a sprint the same way performance requirements do — as acceptance criteria, not as post-release audits.
The operational difference is substantial. In a mature DevOps shop without DevSecOps, a developer might merge code that introduces a hardcoded AWS key, ship it to production, and only discover the exposure when a cloud provider anomaly alert fires two weeks later. In a DevSecOps environment, the pre-commit hook, the PR scanner, and the CI pipeline all catch that key before the merge ever completes.
2. Shift Left Security: What It Concretely Means
"Shift left" refers to moving security activities earlier on the traditional SDLC timeline — which, when drawn on a whiteboard, runs left (requirements) to right (production). The phrase has been repeated so often it has lost meaning for many practitioners. Let me make it concrete.
Here is what shifting left looks like at each stage:
Requirements and Design Phase
Threat modeling happens here, not after the architecture is frozen. I use the STRIDE model as a starting point for most systems: Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege. For a new microservice, this takes ninety minutes in a room with two engineers and one security champion. The output is a list of security requirements that go into the sprint backlog as first-class tickets, not a separate security document that gets filed and forgotten.
Development Phase
IDE plugins surface vulnerability findings in real time. Pre-commit hooks reject commits with secrets or known-vulnerable dependencies. Code review checklists include security items. Developers have access to a self-service security knowledge base — not a ticket queue to a separate team.
Build Phase
SAST runs on every commit. Dependency manifests are scanned against vulnerability databases. Container images are built from vetted base images and scanned before being pushed. Build failures due to security findings are treated the same as build failures due to compilation errors.
Test Phase
DAST runs against staging environments. Integration tests include security test cases — authentication bypass attempts, injection payloads, insecure direct object reference checks. Security unit tests live alongside functional unit tests in the same repository.
Deploy Phase
IaC is scanned before provisioning. Deployment manifests are validated against policy. Secrets are injected at runtime from a vault, never baked into images or environment variable files.
Operate Phase
Runtime security monitoring is active. Anomaly detection fires on unexpected process executions, unusual network connections, and file integrity changes. Incident response runbooks are tested quarterly, not yearly.
Key insight: Shifting left does not eliminate right-side security activities. You still need runtime monitoring and incident response. What changes is the ratio: more findings caught early, where they are cheap to fix, and fewer surprises in production, where they are expensive to remediate and damaging to trust.
3. SAST, DAST, IAST, and RASP: A Practical Comparison
These four acronyms represent the core scanning modalities in a DevSecOps pipeline. Practitioners often treat them as interchangeable when they are fundamentally different in what they observe, when they run, and what they catch.
SAST — Static Application Security Testing
SAST analyzes source code, bytecode, or compiled binaries without executing the application. It runs during the build phase and is well-suited for catching injection flaws, insecure cryptographic usage, hardcoded credentials, and logic errors that are visible from the code alone.
Pipeline placement: Pre-commit (lightweight rules), PR check (full scan), nightly deep scan.
Representative tools in 2026: Semgrep (open source, highly customizable rules), SonarQube (broad language support, good IDE integration), Checkmarx (enterprise, strong SAST depth), CodeQL (GitHub-native, deep semantic analysis).
Limitation: High false positive rates on complex codebases. SAST cannot observe runtime behavior — it misses vulnerabilities that only manifest during execution, such as race conditions or environment-dependent logic.
DAST — Dynamic Application Security Testing
DAST exercises a running application from the outside, probing it the way an attacker would. It sends malformed inputs, attempts authentication bypasses, checks for insecure headers, and fuzzes API endpoints.
Pipeline placement: Against staging or ephemeral test environments, post-deployment in CI. Not suitable for production due to active probing risk.
Representative tools: OWASP ZAP (open source, widely used), Burp Suite Enterprise (excellent coverage, higher cost), Nuclei (template-based, fast).
Limitation: Requires a running application. Coverage depends on the quality of test scripts and crawling depth. Blind to vulnerabilities in code paths that the scanner never exercises.
IAST — Interactive Application Security Testing
IAST instruments the application at runtime — typically via an agent — and observes actual code execution during functional testing. Because it combines the precision of SAST (seeing the code) with the context of DAST (seeing real execution), it generates fewer false positives than either alone.
Pipeline placement: During automated functional test runs in CI.
Representative tools: Contrast Security (strong IAST implementation), Seeker by Synopsys.
Limitation: Coverage is bounded by test coverage. If no functional tests exercise a code path, IAST will not analyze it. Agent overhead adds latency to test runs.
RASP — Runtime Application Self-Protection
RASP lives inside the production application and blocks attacks in real time. Rather than detecting vulnerabilities in code, it detects and intercepts malicious execution at runtime — an SQL injection attempt is blocked before the query reaches the database.
Pipeline placement: Production deployment artifact. Not a CI/CD tool.
Representative tools: Sqreen (now Datadog), Hdiv Security, OpenRASP.
Limitation: False positives can block legitimate requests. Agent overhead in production. Should be a defense-in-depth layer, not a primary vulnerability management strategy.
| Dimension | Traditional Security | DevSecOps |
|---|---|---|
| When security runs | Pre-release gate or periodic audit | Every commit, every build, continuously |
| Who is responsible | Dedicated security team | Every engineer, enabled by security team |
| Vulnerability discovery latency | Weeks to months after introduction | Minutes to hours after introduction |
| Fix cost | High (context switch, re-test cycle) | Low (developer still has context) |
| Impact on delivery speed | Sporadic hard stops | Continuous low-friction friction |
| Developer security awareness | Annual training, low retention | Continuous feedback loop, high retention |
| Tooling integration | Separate security toolchain | Embedded in IDE, CI/CD, and SCM |
| Compliance evidence | Point-in-time snapshots | Continuous, automated audit trail |
4. Secret Detection: Preventing the Most Common Breach Vector
Leaked credentials in source code repositories remain one of the top breach vectors year after year. In my experience auditing repositories, roughly one in four organizations with more than fifty developers has at least one live credential exposed in a non-public or semi-public repository. The number for fully public repositories is far worse.
The problem compounds because Git history is permanent unless explicitly rewritten. A secret committed two years ago by a developer who has since left the company may still be present in the history even if the file was later updated. This is why secret detection must happen at commit time — not just in current file state scans.
GitGuardian
GitGuardian is the most widely deployed commercial secret detection platform in 2026. Its detection coverage spans over 350 secret types — API keys, OAuth tokens, database connection strings, private keys, and cloud provider credentials across AWS, GCP, Azure, and dozens of SaaS platforms. The platform operates as a webhook on GitHub, GitLab, or Bitbucket, scanning every push in real time.
What distinguishes GitGuardian in practice is its low false positive rate and its ability to detect secrets in historical commits during onboarding. For organizations starting fresh, the historical scan report is often sobering — I ran it against a 200-developer codebase and found forty-three distinct live credentials that had been dormant in history for an average of fourteen months.
GitGuardian also provides a developer-facing notification flow: when a developer pushes a secret, they receive an immediate email with remediation instructions. This turns a security finding into a teachable moment rather than a helpdesk ticket.
TruffleHog
TruffleHog is the dominant open-source option, widely used in self-hosted pipelines and air-gapped environments. Version 3 introduced verified detection — for supported secret types, TruffleHog makes a test API call to confirm whether the exposed credential is actually active. This dramatically reduces the remediation backlog: inactive or revoked credentials can be triaged at lower priority, while verified-active findings demand immediate response.
TruffleHog integrates cleanly into GitHub Actions, GitLab CI, and Jenkins. For pre-commit hooks, it pairs well with the pre-commit framework.
Gitleaks
Gitleaks is a fast, lightweight alternative that excels in CI pipelines where scanning speed matters. It uses a rule file format that is straightforward to customize, making it practical for organizations with proprietary secret formats — internal service account tokens, on-premise system credentials, and custom API patterns that generic tools miss.
Practical note: Secret detection tools generate alerts, but alert fatigue is a real failure mode. I recommend routing verified-active findings directly to an on-call Slack channel with auto-assigned tickets, while routing unverified or low-confidence findings to a weekly triage queue. Treating all findings equally causes teams to start ignoring all of them.
5. Dependency Scanning: Managing the Open Source Attack Surface
Modern applications are predominantly open source code. A typical Node.js application has 500–1,500 direct and transitive dependencies. A Java Spring Boot application may have 200–400. Each dependency is a potential supply chain vulnerability, and the attack surface grew significantly after events like the Log4Shell disclosure in 2021 and the XZ Utils backdoor in 2024.
Snyk
Snyk has become the reference implementation for developer-friendly dependency scanning. Its database, which aggregates from NVD, GitHub Advisory Database, and proprietary research, typically adds new vulnerability records 24–48 hours before NVD for high-impact packages. In my testing, this lead time has caught critical vulnerabilities before most organizations have had a chance to respond through other channels.
Snyk's fix PRs are the feature that drives the most developer adoption. Rather than simply reporting a vulnerability, Snyk opens a pull request with the dependency version bumped to a non-vulnerable release, including a changelog summary. Acceptance rate on auto-fix PRs in organizations I have worked with runs between 60% and 80% for non-breaking upgrades.
Dependabot
GitHub's native Dependabot is the zero-friction entry point for teams on GitHub. It requires no setup beyond a configuration file and integrates directly into the repository's Security tab. For teams with straightforward dependency graphs and high test coverage, Dependabot's automated update PRs can be sufficient without a commercial tool.
The limitation I encounter most often: Dependabot does not provide reachability analysis. It will flag a vulnerable function in a dependency even if your code never calls that function. This produces false urgency. Snyk's reachability analysis (in its Enterprise tier) addresses this by tracing call graphs to confirm whether vulnerable code is actually exercised.
OWASP Dependency-Check
For self-hosted, air-gapped, or compliance-driven environments where sending code to a SaaS platform is not acceptable, OWASP Dependency-Check remains the standard. It supports Java, .NET, JavaScript, Ruby, Python, and more. Integration with Maven, Gradle, and Ant is mature. The NVD data feed it uses has suffered from update delays in recent years, which is worth monitoring.
6. Container Security: Every Image Is a Deployment Unit
Containers changed the security model of infrastructure. When every application ships as an image, and images are immutable deployment artifacts, the security of the base image and every layer becomes a first-order concern. A patched OS package in the base image means rebuilding and redeploying — which is operationally straightforward in a well-automated pipeline but requires that the pipeline exists and runs on every image.
Trivy
Trivy, maintained by Aqua Security, is the most widely adopted open-source container scanner in 2026. Its appeal is breadth: it scans OS packages, application dependencies, IaC files, and Kubernetes manifests from a single binary. The database is updated daily, and the scan is fast enough to embed in CI without meaningfully increasing build time.
I use Trivy as the default scanner in most CI pipelines because the integration is minimal and the signal quality is high. A typical configuration sets a CRITICAL/HIGH threshold: builds fail on unfixed critical or high vulnerabilities in the image. Medium and low findings go to a tracked backlog.
Grype
Grype, from Anchore, is a strong Trivy alternative with a slightly different vulnerability database (Grype uses the Anchore vulnerability database, which aggregates from NVD, GitHub Advisories, and OS-specific feeds). The matching algorithm for package detection is often cited as having fewer false positives for certain language ecosystems, particularly Python and Ruby.
Clair
Clair is a container vulnerability scanner from CoreOS (now Red Hat) designed for integration into container registries. Rather than scanning in CI, Clair sits in the registry and scans every image on push. This model is complementary to CI scanning: CI catches issues before the image is pushed, Clair catches issues with images already in the registry as new vulnerabilities are disclosed against existing images.
Container security posture checklist:
✓ Use minimal base images (distroless or Alpine) to reduce attack surface
✓ Never run containers as root — enforce non-root UID in Dockerfile and via PodSecurityStandards
✓ Scan images in CI before push and in registry continuously
✓ Set image pull policy to Always to prevent stale image use
✓ Sign images with Cosign and verify signatures at admission
✓ Enforce read-only root filesystem where possible
7. Infrastructure as Code Security: Catching Misconfigurations Before They Provision
IaC security is where many organizations have the largest gap between their perceived posture and reality. Teams adopt Terraform, CloudFormation, or Pulumi to make infrastructure repeatable and auditable — but without IaC scanning, they automate the replication of misconfigurations at scale. An S3 bucket provisioned with public access enabled, replicated across twelve environments by a Terraform module, is twelve misconfigurations deployed in the time it takes to run terraform apply.
Checkov
Checkov, from Bridgecrew (now Prisma Cloud), is the broadest IaC scanner available in 2026. It covers Terraform, CloudFormation, ARM templates, Kubernetes manifests, Dockerfiles, and Helm charts. The rule library contains over 1,000 checks mapped to CIS Benchmarks, SOC 2, HIPAA, PCI-DSS, and NIST 800-53.
What I value about Checkov is the custom policy framework. Organizations have internal security standards that no commercial rule library covers exactly. Checkov's Python-based custom check API makes it feasible to encode those standards and run them alongside the built-in checks in the same pipeline step.
tfsec
tfsec (now part of Trivy as trivy config) is purpose-built for Terraform and HCL, with deep understanding of Terraform syntax including module resolution and variable interpolation. It catches misconfigurations that simpler pattern-matching tools miss because they do not understand the Terraform evaluation model — for example, a public subnet setting that is only exposed when a variable defaults to true.
Terraform Sentinel
Sentinel is HashiCorp's policy-as-code framework, available in Terraform Cloud and Enterprise. Unlike Checkov and tfsec, which analyze static files, Sentinel runs as a policy check in the Terraform plan/apply workflow, with access to the full plan object — including computed values that are not visible in static analysis. Sentinel policies can enforce cost controls, naming conventions, required tags, and security configurations simultaneously.
The tradeoff is the higher barrier: Sentinel requires Terraform Cloud or Enterprise, and the HCL-based policy language has a learning curve. For teams already on the HashiCorp stack with mature Terraform usage, the investment pays off in policy consistency across all teams using the same Terraform workspace.
8. SBOM Mandates: The Regulatory Landscape in 2026
Software Bills of Materials have moved from a recommended practice to a regulatory requirement faster than most organizations anticipated. The trajectory started with US Executive Order 14028 in 2021, which directed federal agencies to require SBOMs from software vendors. By 2026, SBOM requirements appear in:
- US federal procurement contracts — vendors supplying software to federal agencies must provide SBOMs in CycloneDX or SPDX format
- EU Cyber Resilience Act — effective in phased stages from 2024, mandates SBOM as part of conformity assessment for products with digital elements sold in the EU market
- FDA medical device guidance — cybersecurity premarket submission requirements explicitly include SBOM
- Several Fortune 500 customer contracts — enterprise procurement increasingly demands SBOMs as a vendor qualification requirement
From a tooling perspective, generating an SBOM is not the hard part. Tools like Syft (Anchore), cdxgen, and GitHub's dependency graph all produce compliant CycloneDX or SPDX documents. The harder problems are:
- SBOM accuracy at transitive depth — most tools handle direct dependencies well but become less reliable at three or four levels of transitivity
- SBOM freshness — a point-in-time SBOM is only useful if generated and stored per build artifact, not just once at project creation
- VEX documents — Vulnerability Exploitability eXchange documents supplement SBOMs by stating which known vulnerabilities in components are not actually exploitable in a given product. Generating accurate VEX requires reachability analysis, which remains immature for most languages.
9. Security Champions: Scaling Security Without Scaling the Team
Security teams cannot be everywhere. In a 500-developer organization with a ten-person security team, the ratio makes meaningful engagement impossible if security only flows through central specialists. The Security Champions model addresses this by embedding security-focused engineers within development teams.
In practice, a Security Champion is a developer (or DevOps engineer) who dedicates roughly 10–20% of their time to security activities within their team. They are not security professionals — they do not need to be. Their value is translation: they understand the codebase, they speak the same language as their teammates, and they can surface security guidance at the moment it is relevant rather than in a quarterly review.
Building an Effective Champions Program
The programs I have seen fail share a common trait: they treat Champions as security consumers rather than security contributors. Champions who are given a badge, a Slack channel, and a reading list quickly disengage. The programs that work give Champions real authority and real resources:
- Dedicated time allocation — Champions need protected capacity. Without it, security work crowds out under delivery pressure.
- Budget for tools and training — investing in a Champion's security education (certifications, conference attendance, tool access) signals organizational commitment
- Authority to block PRs — Champions with no enforcement capability cannot drive change; they can only advise
- Regular cohort meetings — Champions learn from each other. A monthly meeting sharing findings, war stories, and new tooling builds the network effect that makes the program durable
- Visible leadership support — engineering managers who communicate that security champion work matters in performance reviews make the program structurally sustainable
10. Reducing MTTR: The Metric That Matters Most
Mean Time to Remediate (MTTR) is the most operationally meaningful security metric I track. It measures the average elapsed time between when a vulnerability is discovered and when a verified fix is deployed to production. Industry benchmarks vary significantly by severity:
- Critical vulnerabilities: target <24 hours, industry median ~12 days
- High vulnerabilities: target <7 days, industry median ~30 days
- Medium vulnerabilities: target <30 days, industry median ~90 days
The gap between target and median reveals where most organizations are. Closing that gap requires attacking the bottlenecks in the remediation workflow, which in my experience fall into three categories:
Bottleneck 1: Discovery-to-Assignment Latency
Findings that sit in a security scanner dashboard without being assigned to an owner do not get fixed. Automation that routes scanner output directly to the team's issue tracker, creates tickets with appropriate severity and SLA labels, and assigns them to the team owning the affected component reduces this latency from days to minutes.
Bottleneck 2: Fix Development Time
Developers who receive a vulnerability report without context spend significant time understanding what to fix and how. Fix guidance embedded in the finding — the specific line of code, the recommended remediation pattern, a link to the relevant CWE — reduces fix development time substantially. Tools like Snyk and Semgrep do this well. Custom SAST rules that do not include remediation guidance do not.
Bottleneck 3: Deployment Pipeline Throughput
A fix that is written but not deployed has not reduced risk. Organizations with infrequent deployment windows accumulate fixed-but-undeployed vulnerabilities in a queue that creates false comfort. Continuous deployment — or at minimum daily deployment capability — is a security control, not just an operational convenience.
11. Minimizing Developer Friction: The Adoption Problem
The single most common DevSecOps failure mode I encounter is not technical — it is adoption failure driven by friction. Security tools that block developer workflows without providing clear, actionable feedback get circumvented. Teams that can bypass security gates will bypass them under deadline pressure, and once bypassing is normalized, the controls provide only the appearance of security.
Designing for low friction means:
Fast Feedback
Security findings that arrive in a separate email the next morning are ignored more often than findings that appear as inline PR comments within ninety seconds of a push. Speed of feedback directly correlates with developer engagement with findings. Invest in making scans fast — parallelization, incremental scanning (only scan changed files), and caching scan results for unchanged dependencies all help.
Actionable Findings
A finding that says "CWE-89: Improper Neutralization of Special Elements used in an SQL Command" is not actionable for a developer unfamiliar with CWE taxonomy. A finding that says "Line 47: Parameterize this query to prevent SQL injection — see example below" is. The difference in remediation rate is large. When configuring scanner rules, prioritize tools and rule configurations that include remediation examples.
Meaningful Severity Tiers
If every finding is HIGH, developers learn to ignore the severity label. Calibrating severities to actual exploitability and actual business impact — rather than CVSS score alone — requires effort but produces a signal that developers respond to. A critical-severity finding in a function that handles payment card data is materially different from a critical-severity finding in an internal admin tool with two users. Both may have the same CVSS score; their business impact is not equivalent.
Graceful Enforcement Ramp
When rolling out a new scanner, start in warn-only mode. Let findings accumulate for two to four weeks so developers become familiar with the tool and the baseline finding rate. Then enforce blocking only on new findings introduced after a cutoff date. This avoids the situation where turning on a new scanner immediately blocks every PR in the organization, which creates hostility toward the tool and the security team.
From my experience: At one organization, enabling blocking enforcement on day one caused 34 PRs to fail simultaneously. The backlash set the DevSecOps program back six months. At the next organization, a four-week warm-up period with daily summaries to the team led to voluntary remediation of 60% of the backlog before enforcement was even activated. The technical configuration was identical; the rollout strategy made all the difference.
12. Pipeline Architecture: Putting It All Together
A complete DevSecOps pipeline in 2026 has multiple security layers that complement each other without duplicating effort. Here is the architecture I recommend for most greenfield implementations:
Pre-Commit (developer workstation)
- Secret detection: Gitleaks or TruffleHog via pre-commit framework
- Lightweight SAST: Semgrep with a curated subset of rules (~2-second scan time target)
- Dependency advisory check: local cache of known-vulnerable versions
PR / Merge Request Check (CI, per-commit)
- Full SAST: Semgrep or CodeQL on changed files, with incremental scanning
- Secret detection: GitGuardian webhook or TruffleHog CI action
- Dependency scan: Snyk or Grype against updated manifest
- IaC scan: Checkov or tfsec on modified IaC files
Build (CI, on merge to main)
- Full dependency scan including transitive dependencies
- Container image scan: Trivy on built image before push
- SBOM generation: Syft or cdxgen, stored as build artifact
- Image signing: Cosign
Deploy (CD pipeline)
- Admission control: OPA Gatekeeper or Kyverno policy check
- IaC policy: Checkov or Terraform Sentinel on full plan
- Image signature verification at admission
Runtime (production)
- Runtime security: Falco or Tetragon
- Continuous container scan: Clair or Trivy in registry
- DAST: scheduled ZAP scan against staging
13. Key Takeaways
Seven things I believe to be true about DevSecOps in 2026:
1. Culture precedes tooling. The most sophisticated scanner stack fails if developers are incentivized to ship fast and penalized for raising security blockers. Fix the incentive model first.
2. Secret detection at commit time is non-negotiable. Every organization should have this running before any other control. It is cheap, high-signal, and prevents a class of breach that is otherwise entirely avoidable.
3. Shift left does not mean shift only left. Runtime detection catches what static analysis misses. Both layers are necessary.
4. MTTR is a better program health metric than finding count. Accumulating findings without remediating them is not progress; it is a false sense of visibility.
5. Security Champions scale what central teams cannot. A well-run champions program multiplies security capacity without proportional headcount growth.
6. SBOM compliance is already a sales-blocking issue in regulated industries. If you sell to federal agencies, healthcare organizations, or large financial institutions and do not have SBOM generation in your pipeline, you are already at a disadvantage.
7. Developer friction is the primary adoption risk. Every unnecessary false positive, every blocked PR without clear remediation guidance, and every slow scan erodes trust in the program. Treat developer experience as a product requirement for your security toolchain.
Conclusion
DevSecOps in 2026 is not a single tool purchase or a team rename. It is a set of engineering practices that treat security findings with the same urgency and the same continuous improvement mindset that good teams apply to test failures and performance regressions. The organizations that have internalized this — where a developer naturally reaches for a threat model before designing a new service, where a secret in a PR is as unacceptable as a failing test — are the ones accumulating both delivery velocity and security posture over time, rather than trading one against the other.
The tooling landscape covered in this article is mature. The hard work is the organizational change: getting security into sprint planning, getting security findings into developer workflows, and getting developers to see security as part of the quality bar they already care about — not as someone else's problem.
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