Lean teams can make meaningful progress on product security without starting with a large department or a new platform—but only if they first map what they need to protect, put controls where engineering decisions happen, and give someone responsibility for acting on findings. Ivan Piskunov draws those lessons from 15 anonymized engagements. His figures describe that small sample, not the software industry as a whole.
What the 15 engagements found—and what the figures mean
Piskunov’s account describes recurring gaps in foundational controls, configuration, security workflow, and measurement. The sample spans startups, scale-ups, and growing software companies. Because the underlying engagements are anonymized and the sample is small, the rounded percentages are directional observations, not estimates of how often these problems occur across the market. As Piskunov puts it, “This is not intended to be an industry benchmark.”
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| Observation in the sample | Reported result | How to read it |
|---|---|---|
| Foundational controls were missing, outdated, or poorly aligned | About 40%, or roughly 6 of 15 engagements | Piskunov’s approximate count in this sample; not a market prevalence rate. |
| Configuration artifacts contained defaults or security-relevant misconfigurations | More than 60 artifacts | Artifacts covered infrastructure, CI/CD, cloud, Kubernetes, and services; this is not a count of affected companies. |
| Teams initially expected meaningful product security to require major investment | About 70% | An observation about teams’ initial expectations, not the measured cost of a security program. |
| Teams had limited security automation, aggregation, ownership context, or useful metrics | About 80–85%, or roughly 12–13 of 15 engagements | A rounded description of this sample, not a claim that most companies have the same gap. |
These observations point to a practical distinction: having a scanner, policy, or cloud control is not the same as knowing whether it covers the important systems, produces actionable results, and leads to a verified fix. Piskunov summarizes the configuration problem this way: “Working configuration is not the same as secure configuration.”
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Security scope tends to grow as a company adds repositories, cloud accounts, services, APIs, pipelines, identities, and customer requirements. A baseline begins with an inventory that shows how those pieces connect—not simply a list of tools or assets.
#1 Best Overall
- Products and components: identify critical applications, APIs, services, repositories, and workloads.
- People and identities: map human access, workload identities, IAM roles, and privileged paths.
- Delivery paths: trace code from development through CI/CD into production, including who or what can approve and deploy changes.
- Data and exposure: locate sensitive data stores, external entry points, cloud resources, Kubernetes environments, and third-party connections.
- Trust boundaries: note where privileges, data, or control pass between components or organizations.
This view helps teams prioritize attack paths and exposed systems rather than treating application code, cloud configuration, identity, and third parties as unrelated checklists. It also reveals whether a control is absent, applied inconsistently, or simply disconnected from the system it is meant to protect.
Make secure configuration a repeatable default
In the engagements, security-relevant configuration appeared across infrastructure, Kubernetes, CI/CD, application services, access policies, and infrastructure-as-code. Examples included overly broad permissions, unnecessary network exposure, production debug behavior, weak policy enforcement, unmanaged secrets, and unsuitable defaults. These are not problems a one-time review can reliably prevent if each team rebuilds the same environment differently.
Use reusable templates and hardened baselines for common patterns, then express important settings as configuration-as-code where practical. Add automated checks close to the changes they evaluate. Enforce policies selectively: a gate should block a genuinely unacceptable risk, while lower-risk findings can be routed for review without making every build noisy. Give teams a clear path to request and expire exceptions.
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Sequence spending around risk, not tool count
Many teams in Piskunov’s account initially associated meaningful security with expensive platforms, a dedicated department, several engineers, or large outsourcing projects. His alternative is to understand the product and its risks before deciding what to buy. Cloud, code-hosting, and CI/CD services may already include capabilities a team can use; mature open-source projects may cover some workflow needs. That does not mean existing or open-source capabilities are automatically sufficient—it means assess them before adding overlapping tools.
- Understand the product, architecture, important data, users, and delivery paths.
- Identify the risks that could materially affect the product or its customers.
- Choose the smallest useful set of controls that addresses those risks.
- Automate repeatable decisions and checks where they fit the workflow.
- Buy or add technology when it fills a demonstrated gap or improves an existing control.
When comparing possible controls or investments, assess risk reduced, workflow timing, architectural fit, coverage, ownership, operational noise, and the evidence produced. Check whether the capability already exists in a service the team uses. A tool that creates alerts without context, owners, or a closure process can increase activity without reducing risk.
Rank #3
Turn scattered findings into owned decisions
Security information can be spread across scanners, cloud alerts, tickets, spreadsheets, CI/CD output, vulnerability exports, dependency data, and infrastructure findings. Aggregation helps only when it adds context: which product or asset is affected, how important it is, who owns remediation, and what action is due.
Establish responsibility for remediation, escalation, risk acceptance, exception expiration, and verification. Define closure criteria so that a ticket is not considered resolved merely because it was closed; the team should know what evidence demonstrates that the risk was addressed or consciously accepted. Piskunov’s principle is concise: “A finding without an accountable owner is information—not risk reduction.”
Choose measures that answer decisions leaders and engineers actually face. Possible measures include control coverage, risk debt, remediation velocity, exception health, release confidence, vulnerability aging, and ownership. They are useful only when definitions are consistent and the result changes a priority, investment, release decision, or remediation action. As Piskunov writes, “Measure what changes a decision—not what is easiest to count.”
Place checks where they can still change the outcome
A security check that arrives after architecture or release decisions are settled can make remediation slower and more costly. Put each control near the decision it is intended to inform: design review for architecture risks, code and dependency checks during development, infrastructure checks before deployment, and access or exposure reviews where permissions and production boundaries are managed.
Review the path across components, not only each component in isolation. An application, API, CI/CD pipeline, workload identity, IAM policy, secret store, cloud resource, Kubernetes cluster, third party, or data store may each appear acceptable on its own while a connection between them creates an important attack path.
A practical 30/60/90-day starting sequence
Piskunov suggests a staged plan for a bounded product and cloud environment. It is an implementation sequence, not a promise of complete security in three months.
Best Value
Days 1–30: establish the map
- Identify critical products, repositories, identities, cloud accounts, sensitive data, delivery paths, and obvious external exposure.
- Record important trust boundaries and the people or teams responsible for each area.
- Use the map to select a short list of meaningful risks and gaps in foundational controls.
Days 31–60: add controls to engineering workflows
- Integrate relevant SAST, SCA, secrets, infrastructure-as-code, and container checks into the workflows where they can influence decisions.
- Establish secure configuration patterns that teams can reuse.
- Set risk-based release gates and assign owners and closure criteria for vulnerabilities.
Days 61–90: validate, measure, and choose what comes next
- Measure control coverage and remediation using consistent definitions.
- Validate critical attack paths and review open exceptions, including their owners and expiration.
- Create reusable evidence of how controls operate, then select the next priorities based on observed gaps and risk.
For a bounded product and cloud environment, Piskunov says a meaningful baseline may take roughly 90–100 days with a two-engineer or fractional-security model. That is his experience-based guidance, not an externally validated delivery benchmark; architecture, complexity, and implementation capacity can change the timeline.
How to tell whether the effort is working
Ask whether the work is reducing important risk, whether controls are operating as intended, whether critical issues are closing faster, and where the next engineering hour or dollar should go. Evidence should connect activity to those decisions: coverage of critical assets, time to remediate prioritized issues, unresolved ownership, overdue exceptions, and validated control operation. Counts of scans, alerts, or tickets alone cannot establish that the product is safer.
The direction of Piskunov’s advice is to begin narrowly, solve a real product risk, and expand when the evidence supports it: “Start narrow. Solve the real problem. Expand with evidence.”
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