Security testing services are designed to expose risk. Cloud security audits check your AWS, Azure, or GCP setup for misconfigurations, weak access control, and unsafe defaults. Application security testing examines web and mobile apps through scans and manual review to catch SQL injection or broken authentication. Vulnerability assessments run structured scans to map known weaknesses, but stop at identification. Penetration testing pushes further: controlled attacks that show exactly how far an attacker could get, with real impact mapped to the actual exposure.
The article is based on case studies of a software testing company, Belitsoft. This firm has been providing security testing services for 20+ years worldwide. Belitsoft’s expertise is proven by the 4,9/5 score on Goodfirms, Gartner, and G2 from clients that have partnered with the company for more than 5 years. Belitsoft has a wide pool of security testing experts with rich backgrounds in multiple types of such services, including healthcare penetration testing, CRM security testing, or testing for companies in the security domain. They know the peculiarities of each domain, ensuring all potential threats are detected instantly. Belitsoft’s team will assess your systems, networks, and APIs to eliminate all risks and deliver secure solutions.
Penetration Testing
Penetration testing is used to simulate real-world attacks on live systems. Fintech teams run it to validate compliance with PCI DSS and test transaction-level exposure. In healthtech, it used to assess ePHI protection under HIPAA. SaaS teams rely on it to pass SOC 2 audits and meet enterprise client requirements. The reason may vary, but the mechanism is the same: find weaknesses before someone else does.
A test only adds value if the setup is right. That starts with scope: what’s in, what’s out, what infrastructure is included, and who owns it.
Team alignment isn’t optional. Engineering department should not find out about the test because they’re paged at midnight. They need to know what’s being tested, why it matters, and what happens if it breaks.
Vendor selection is about the people doing the work. Ask who’s testing, what their experience is, and how they map their approach to your environment. Large firms sometimes rotate vendors to get broader coverage – application security, infrastructure, social engineering. Startups often scope tightly and use a PTaaS model for continuous input without the overhead of full consulting.
Notify your cloud providers: AWS, Azure, GCP all have protocols for time the test to avoid peak load. Staging is safer, but only if it matches production. Patch systems you already know are vulnerable. Tell your SOC to expect a scan load. Assign a live point of contact who can authorize a pause. Pen testing is a controlled break but only if the brakes work.
Good vendors provide interim updates. They flag critical issues on day one. They surface blockers early.
Post-test behavior matters more than execution. The report should be reviewed by engineering, security, and leadership together. Findings need owners. Criticals need the fix windows. Re-tests should be scheduled up front. Metrics should be tracked over time: time-to-fix, recurring issues, regression rate.
Clients expect full methodology transparency: PTES, OWASP, MITRE ATT&CK, and NIST 800-115. Reports should map to compliance frameworks where needed (PCI DSS, HIPAA, SOC 2) and cover not just what was tested, but what wasn’t, and why.
Vulnerability Assessments
Penetration testing is episodic. Vulnerability assessment is ongoing. Organizations that treat vulnerability assessments as routine maintenance, not a quarterly checkbox, avoid coverage gaps and reduce response lag. They know what assets they have, scan those assets consistently, and fix what matters first.
High-functioning teams map asset changes to scanning automation. They log every system that stores sensitive data (patient records, financial transactions, user credential) and tie each to scanning coverage.
Monthly external scans and weekly internal scans are typical. But the cadence is determined by risk. If SaaS platforms release daily, and containers are rebuilt hourly, then waiting a month to scan will guarantee threats. Automation is the fix for triggering scans on push, on build, on deploy.
CI/CD-integrated scans are now a standard pattern.
Every scan produces hundreds of issues. Without prioritization, none of them matter. The volume is too high, and the difference between informational and urgent is not obvious. Risk-based models are common: CVSS score × business impact × exposure. Application of those models is what separates mature approaches. A fintech organization testing transaction systems prioritizes differently than a SaaS firm scanning internal admin tools.
Fixing security issues in production is slow, visible, and expensive. Fixing them before the code ships is fast, contained, and invisible to users. Mature teams integrate scanning into development: static analysis in PRs, dependency scans on every push, IaC checks before application, container validation on build. This catches misconfigurations before they deploy. It also gives developers direct feedback on what security expects and prevents the need for additional meetings.
Automated scanning catches known vulnerabilities. Most organizations supplement scanning with periodic manual validation. They target applications with changing threat models, sensitive business logic, or high exposure.
Strong testing teams integrate scanning tools into issue management systems (Jira, ServiceNow, ticket queues) used by the actual fix teams. Findings are assigned owners. Deadlines are attached. Fixes are verified.
NIST SP 800-40, CIS Controls, ISO 27001 – these are set default expectations for coverage frequency, patch windows, and reporting. Mature teams align their scan cadence, patch SLAs, and exception policies to these frameworks by default.
Cloud misconfigurations, orphaned services, permissive IAM roles – these are exploited often. Kubernetes clusters should be scanned for misconfigured RBAC. Terraform templates should be validated pre-deploy. AWS environments should be assessed for open buckets, overly permissive roles, and public endpoints.
Mature teams track MTTR for critical and high findings, percentage of scoped assets scanned, vulnerability recurrence by asset or team, SLA adherence by team and severity, framework pass rates (CIS, PCI DSS, HIPAA), incident correlation (was this preventable?). These metrics prove progress. And in regulated environments, they often prevent budget attrition.
Application Security Testing
Application security testing isn’t just a phase or a budget line. For teams under pressure to move fast, add features, ship to prod, application security testing isn’t about scanning for bugs. It’s about knowing when the app will fail, why, and what the blast radius looks like when it does. The difference between teams that test for security and teams that build with security is when they think about it, not what tools they use.
Static analysis tools don’t catch runtime behavior. Dynamic scans miss broken logic. Software composition tools alert on CVEs that nobody patches until a crisis lands in Slack. So high-performing teams don’t pick one method. They layer. SAST on commit. DAST in staging. SCA in CI. Manual review on sensitive flows.
But none of this matters if it’s tacked on at the end. If security runs as a weekly ticket or a post-release checklist, you’re just doing regression for threats you already know. Real application security testing means embedding it in the CI pipeline: push code, trigger scan, fail build. Developers fix issues while they’re still fresh. One SaaS team moved from quarterly reviews to per-merge scans and saw vulnerability fix time drop by 70%. Not because the scanners improved, because the process changed.
APIs get their own treatment. They expose too much and hide too little. Discovery comes first: undocumented endpoints are liabilities. Then testing for rate limits, authentication lapses, data leakage. Many teams don’t even know what their APIs expose until an external audit surfaces it. The ones who do bake API scanning into their regular release cycle not as a special project, but as another gate between staging and prod.
Tooling isn’t the problem. Coordination is. Red teams find chained exploits because nobody else thinks in chains. That’s the point of simulation: not to check if your scanner works, but to find what nobody’s modeling. One red team combined a harmless UI bug with an API token misconfiguration and pulled down full user datasets. It wasn’t clever. It was obvious, just not to the people who built it.
Security only works when engineering understands why the tests exist. Otherwise you get pushback, slow fixes, and shallow adoption. So successful orgs build security fluency before they enforce anything. They train devs. They elevate security champions inside squads. They explain why one injection is exploitable and another isn’t.
And when the tools overwhelm? Teams should tune. One client saw 900+ findings on the first SAST run. After rule tuning and false positive suppression, that dropped to 22. From useless to actionable. No dev wants another ticket unless it means something.
The same rules apply to supply chain risk. If you use open source, you own the consequences. Modern teams use SBOMs not because the government says so, but because it’s the only way to know if something is hiding in your stack.
Threat modeling should come before a single line of code. What will attackers go after? What matters most if it breaks? Teams that model threats early end up testing the right things. Not just Top 10 issues but business logic failures, role abuse paths, data mishandling that doesn’t crash the app, but leaks the wrong row to the wrong user.
Metrics track whether it’s working. Pre-production catch rate. Time to fix. Recurring vulnerabilities. Ratio of security bugs caught internally vs. externally. Enterprise track escape rate: how many bugs make it to prod.
Cloud Security Audits
The teams who win audits don’t keep a doc, they run a script. When asked “how do you know your S3 buckets aren’t public?” they don’t describe a policy, they show the output.
Logging is where confidence lives or dies. If your logs are missing, your answers are guesses. CloudTrail, Activity Logs, access logs, IAM trails – they all need to feed into a central place that someone can query without begging an engineer. Most teams think they have logs until the auditor asks who accessed a database last Tuesday.
Security guardrails need to be automatic or they won’t exist. CI pipelines that block unencrypted storage, reject open ports, or flag policy drift aren’t nice-to-haves. They’re the difference between “we didn’t catch that” and “we didn’t allow it.” DevSecOps isn’t a culture statement.
The teams that survive audits are the ones who assume every role is too permissive until proven otherwise. They rotate keys. They kill dormant accounts. They force MFA. One client got burned when an old role had s3:* (full access) still in place from a migration three quarters ago. The fix was during the access review mandate baked into the sprint calendar.
Frameworks only matter when they’re mapped. CIS Benchmarks, CSA CCM, ISO 27001, SOC 2 – whatever governs your cloud security posture needs to be visible in your environment, not just in a slide deck. Smart teams don’t show auditors that they know the standard – they show how their environment reflects it.
Pre-audits are the dry runs that catch the easy misses: the open security group, the unused admin credential, the storage bucket with test data nobody masked.
Cloud breaches don’t happen because misconfigurations are easy. Audit-grade cloud security means assuming that anyone with console access is a risk factor. The only safe environments are the ones where people can’t click anything.
Multi-cloud isn’t an excuse for inconsistency. The teams who succeed define baseline controls (MFA on admin accounts, encryption at rest, audit logs enabled) and then enforce them with the tooling of the provider. Different syntax, same standard.
Audits are about evidence. Can you prove the alert fired? Can you show who got it, when, and how they responded? Your incident response plan isn’t complete until you’ve simulated cloud-specific failure modes – rogue API key, compromised role, misconfigured logging. Simulations expose process gaps long before an attacker does.
Tools don’t pass audits. Documentation does. A story that explains how your architecture enforces policy, limits exposure, and detects abuse. If your team can’t articulate it, the tools won’t either.
Auditors don’t expect perfection but ownership. That means acknowledging weak spots, showing mitigation plans, and responding fast. It’s not the number of issues – it’s how many you already knew about and how fast you fixed them.
About the Author:
Dmitry Baraishuk is a partner and Chief Innovation Officer at a software development company Belitsoft (a Noventiq company). He has been leading a department specializing in custom software development for 20 years. The department has hundreds of successful projects in such services as healthcare and finance IT consulting, AI software development, application modernization, cloud migration, data analytics implementation, and more for US-based startups and enterprises.
