Security in code has stopped being a peripheral concern and has become an architectural requirement: it’s not something you “add” at the end of a project, it’s the way you model, write, and deliver software. The growing focus on secure coding today is a practical reaction to three realities: increasingly interconnected applications, massive reliance on third-party libraries, and the exponential cost of fixing defects after code hits production. This shifts technical and organizational priorities — and it’s worth explaining why, how to act, and which traps to avoid. The first point is thinking about security from the design stage. Security doesn’t start at input/output checks or at the WAF; it starts in data modeling, in boundaries of responsibility between components, and in trust assumptions. In any distributed system, ask two basic questions when designing an interface: what happens if a malicious client sends arbitrary data? and what privilege does each component truly need to operate? Answering those questions changes simple decisions — for example, separating data envelopes, validating strictly at the edges, and applying resource-level authorization instead of just route-based checks. These choices reduce the attack surface without sacrificing productivity. Input validation and output encoding remain pillars. But validation must be purposeful: distinguish semantic rules (e.g., “field CPF must have 11 digits”) from security invariants (e.g., “field id must not allow traversal or injection”). Validation only on the front end is theater. Anything that impacts trust or system state must be validated on the server. Encoding belongs to the context: HTML-encode for web pages, JSON-escape for API payloads, use prepared statements for databases. Mixing those contexts is how XSS and injection vulnerabilities are born. Dependency management and supply chain security have become priorities. Modern projects don’t write everything; they integrate hundreds of packages. That requires practices: maintaining a dependency inventory, using vulnerability scanners integrated into CI, applying automatic update policies with human review for critical packages, and isolating dependencies when possible (sandboxing). A single vulnerable package in production may force rollbacks of entire features if no mitigation strategy exists. Discipline in reviews and testing has also evolved: linters and unit tests are necessary but insufficient. Integrate static analysis (SAST) to catch insecure code patterns, dynamic analysis (DAST) to validate runtime behaviors, and fuzzing for components that process malformed data. Combine this with focused human review: traditional reviews often overlook security because style dominates. Create explicit security checkpoints in pull requests, with templates requiring answers about validation, authorization, and dependencies. Authentication and authorization deserve careful handling. Authentication benefits from established standards — use proven frameworks and protocols (OAuth2, OIDC) instead of rolling your own flows. Authorization should be expressed as policy, not scattered conditionals: centralize rules in a service or library, apply “deny by default,” and follow least privilege. Sessions need proper renewal, reasonable expiration, and protection against fixation; cookies should carry secure flags (HttpOnly, Secure, SameSite), and tokens should be rotated. Cryptography: never invent algorithms; use primitives from standard libraries or well-maintained ones. Pay attention to details that break security: random number generation (use a CSPRNG), key storage (never in source code), key lifecycle management (rotation, revocation), and choice of modes/parameters (prefer AEAD like AES-GCM, use unique salts and adequate iterations in KDFs such as PBKDF2/Argon2). Common mistakes: using MD5/SHA1 for signatures, using ECB, storing secrets in public repositories. Memory and security. In managed languages you get protection against many errors, but you still face logical vulnerabilities, insecure deserialization, and native dependency risks. In C/C++ and unmanaged languages, buffer overflows, use-after-free, and other memory issues remain major attack vectors. If the project demands native performance, invest in fuzzing and memory analysis tools in the CI pipeline. Main practical vectors to avoid include: injection (SQL/NoSQL/command), XSS, CSRF, broken access control, accidental exposure of sensitive data, and lack of protection against automation (rate limiting, bot detection). For database injection, the principle is straightforward: never concatenate strings to form queries. Use parameterized queries or ORMs that expose secure APIs. A minimal example: ```python # minimal example — use parameters instead of string formatting cursor.execute("SELECT id, name FROM users WHERE email = %s", (email,)) ``` This trivial substitution eliminates most classic SQL injection cases. The same principle applies to system commands, NoSQL queries, and file paths: parameterize, escape properly, or restrict values with whitelists. Observability and response. Security isn’t just prevention; it’s the ability to detect and react. Structured logs (without secrets), authentication/authorization metrics, alerts for anomalous patterns, and incident response playbooks are essential. Periodic simulations (red team / purple team) uncover gaps that automated tests won’t. And remember: many teams delay telemetry due to cost concerns, but the lack of it creates far greater costs during incidents. Culture and training. The biggest security gains come when the entire team understands trade-offs. Small rules with real code examples — such as “use parameterized queries” or “every PR touching authentication must complete the security checklist” — produce concrete results. Invest in training with real code, attack/defense exercises, and in promoting shared responsibility across developers, SRE, and product. Finally, the economics of security: fixing a flaw during planning is cheap; after deployment and public exposure, the cost skyrockets — emergency patches, communication, reputation damage, and possible fines. It’s no exaggeration to say secure coding is both long-term cost savings and a hallmark of mature engineering.