Articles

API keys are the most-leaked credential type. Treating their lifecycle as a tracked property — issued, scoped, rotated, revoked — is the difference between hygiene and incident.

Static SSH keys + bastion hosts is the 1990s model. Teleport / Boundary broker access dynamically, record sessions, and integrate with identity. The 2026 default.

Tabletops without follow-through are theatre. Chaos security drills make findings unavoidable. Both, run together, build organizational muscle for real incidents.

Rotation isn't just minting a new secret. It's a sequenced operation across producers, consumers, and stale-credential drains. Most outages happen during rotation.

Cryptographic workload identity that survives across Kubernetes clusters, cloud accounts, and on-prem hosts. SPIFFE replaces shared secrets with attestation.

Threat modeling does not scale by adding more whiteboard sessions. Codify the methodology, embed in design review, and treat threat models like code.

NIST finalized ML-KEM and ML-DSA in 2024. Harvest-now-decrypt-later is already happening. A migration plan that covers TLS, SSH, artifact signing, and secrets is now tractable.

Nearly 80% of intrusion detections in 2026 are malware-free. Attackers steal valid credentials, hijack session tokens, exploit federated access, and bypass weak MFA to move laterally without triggering traditional malware detection. This article covers the defensive controls for identity-based attacks.

Ransomware has evolved from simple encryption to multi-stage extortion: data theft, encryption, public exposure threats, and DDoS. Ransomware-as-a-Service groups operate with dedicated negotiation teams and support desks. This article covers the defensive architecture that reduces blast radius, detects early-stage ransomware behaviour, and enables recovery without paying.

"Are we more secure than last month?" is a question most teams cannot answer. Security tools produce individual outputs: kube-bench returns a CIS score...

Manual compliance audits are point-in-time snapshots that are outdated before the report is written.

PostgreSQL defaults prioritise developer convenience over security. A stock installation on most distributions allows local trust authentication (any.

A fresh Kubernetes cluster (whether bootstrapped with kubeadm, k3s, or provisioned by a managed provider) ships with defaults optimised for getting...

During an active security incident, hardening is reactive: isolate the compromised system, contain the blast radius, preserve evidence, and stop the..

When your security infrastructure fails, you are flying blind. If Vault is down, applications cannot retrieve secrets and new deployments stall.

Self-hosted Prometheus consumes 500GB+ storage within 6 months for a 20-node Kubernetes cluster.

Message brokers carry some of the most sensitive data in any architecture, payment events, user actions, system commands, PII in event streams.

Running infrastructure across multiple cloud providers means maintaining consistent security controls across fundamentally different systems.

Perimeter security assumes the internal network is safe. It is not. A single compromised pod, a stolen VPN credential, or a malicious insider gives...

A team of 1-5 engineers cannot implement 100 hardening controls simultaneously. Most hardening guides present controls as equally important, leaving...

Self-managed Kubernetes clusters (kubeadm, k3s, kops) consume 8-16 hours per month of engineering time for control plane maintenance: etcd backups,...

Redis defaults prioritise developer convenience: no authentication, no TLS, all 200+ commands available, and binding to all interfaces.

CSI drivers run with broad privileges by design. Their security posture often goes unaudited — until one is the exfil path or the privilege-escalation step.

Native Kubernetes Secrets are visible to anyone with namespace get. External Secrets Operator pulls from your real secret store on schedule, with rotation and audit.

restartPolicy: Always init containers GA'd in 1.29 fix the long-standing init/main race. Bigger security wins for service-mesh and log-shipper deployments.

Confidential Containers move workload isolation from the kernel to the silicon. Encrypted memory, hardware-attested boot, and a different threat model than user namespaces.

userns: true remaps Pod UIDs into a per-Pod range. A container running as root sees uid 0 inside; the host sees an unprivileged user. Big hardening win, easy to enable.

VAP replaces webhook admission for the policies you write most often. No Kyverno, no OPA, no network round-trip, no webhook availability risk.

Gateway API replaces Ingress with a multi-role model that separates infrastructure, cluster operator, and application developer concerns. New surface, new threat model.

Kubernetes orchestrates LLM workloads but has no awareness of what those workloads do. An Ollama pod with healthy readiness probes and stable resource usage can still leak secrets, execute prompt injection, and grant models excessive agency over internal services. This article covers the LLM-specific threat model for Kubernetes and implements an LLM gateway as the policy enforcement layer.

A Kubernetes node is a Linux machine running kubelet, a container runtime, and your workloads.

Multi-tenant GPU sharing without isolation risks data leakage between workloads through shared GPU memory.

GPU compute costs between $2 and $30 per hour per device. A single unauthorised cryptocurrency mining pod running on an A100 for a weekend generates..

Request-count rate limiting fails for LLM workloads because a single request can consume 100K tokens. Token-based rate limiting with per-user quotas and abuse detection prevents runaway costs and catches prompt injection probing before it escalates.

Prevention-only security has a binary failure mode: either the control holds and the attacker is stopped, or the control fails and the attacker...

By default, every pod in a Kubernetes cluster can communicate with every other pod across all namespaces. There are no network boundaries.

Without enforced budgets, a single team can exhaust an organization's entire AI spend in days. Token metering with per-team budgets, automatic request rejection at limits, model routing by cost, and chargeback dashboards turn LLM spending from a surprise into a managed line item.

The kubelet runs on every node in the cluster with root-level access to the container runtime, all pod specifications, mounted secrets, and the host..

RBAC sprawl in multi-team Kubernetes clusters grows past 100 role bindings within months.

Kubernetes Secrets are base64-encoded, not encrypted. Anyone with RBAC read access to secrets in a namespace can decode every credential stored there.

When a traditional application causes an incident, you examine logs, traces, and database queries to reconstruct what happened.

Model inference endpoints are GPU-backed and expensive, $2-30 per hour per GPU. A single unprotected endpoint exposed to the internet can accumulate..

Without admission control, any user with deployment permissions can run privileged containers, mount the host filesystem, use the host network, run...

AI systems leak data in ways traditional applications do not. A language model trained on customer data can reproduce verbatim customer records in...

JupyterHub is a code execution platform. Every notebook cell is arbitrary code running with whatever permissions the notebook server process has.

Kubernetes namespaces provide logical separation, not security isolation. By default, pods in namespace A can send network traffic to pods in...

A single content filter is not a pipeline. Most LLM deployments add one filter (usually on output) and call it done.

Traditional security testing (penetration testing, vulnerability scanning) does not cover AI-specific attack surfaces.

Without image policy enforcement, any container image from any registry can run in a Kubernetes cluster.

Retrieval-Augmented Generation (RAG) adds a knowledge base to LLM applications, the model retrieves relevant documents before generating a response.

Kubernetes SecurityContext has over 15 configurable fields, but most teams only set runAsNonRoot: true and consider the job done.

Vector databases are the backbone of RAG (Retrieval-Augmented Generation) systems.

Deploying a new ML model version is not the same as deploying a new application version.

The API server is the front door to the Kubernetes cluster. Every kubectl command, every controller reconciliation, every pod scheduling decision,...

The default container runtime allows approximately 300 syscalls. A compromised container can use unshare to create new namespaces, clone to spawn...

Kubernetes Secrets are stored in etcd as base64-encoded plaintext. Base64 is an encoding, not encryption.

Deploying an LLM without guardrails is deploying an application where any user can make it say or do anything.

The ingress controller is the internet-facing entry point to a Kubernetes cluster.

Traditional application monitoring (CPU, memory, HTTP status codes, latency) tells you nothing about what an LLM is doing.

Model serving frameworks ship with defaults optimised for development: management APIs exposed on all interfaces without authentication, model files..

Fine-tuning pipelines are high-value targets. They consume expensive GPU hours, process proprietary training data, and produce model checkpoints that...

The Kubernetes scheduler places pods on nodes based on resource availability and basic constraints.

Kubernetes audit logs record every request to the API server: who made the request, what they asked for, and whether it succeeded.

Model files are opaque binaries ranging from 1GB to over 1TB. You cannot code-review a set of weights.

Reinforcement Learning from Human Feedback (RLHF) pipelines introduce unique security surfaces that standard ML training workflows do not have.

AI services have turned API keys into direct spending controls. A leaked OpenAI or Anthropic key can generate thousands of dollars in charges within...

Prompt injection is the SQL injection of AI systems, the most common and most damaging attack class against LLM-powered applications.

AI training clusters frequently share networks with production services. A training job that can reach the production database is one compromised...

LLM-powered applications have unique observability requirements that standard APM tools do not address: token-based cost tracking (not just request...

Model registries are the bridge between training and production. A model pushed to the production registry gets served to users.

Every pod in Kubernetes receives a service account token by default. In clusters running older configurations or without explicit hardening, these...

dm-verity gives you a read-only root that fails to mount if a single block is tampered with. dm-integrity adds runtime checksumming. Together: immutable, evidence-bearing systems.

lsm_bpf attaches eBPF programs to LSM hooks. Define security policy in code, push without reboot, audit at the syscall boundary. AppArmor for cloud-native systems.

USB devices are a peripheral attack surface most servers ignore. USBGuard provides allowlist-based authorization, blocking BadUSB and malicious-cable threats.

ed25519-sk and ecdsa-sk bind SSH keys to a hardware token. Phishing-resistant, exfiltration-proof, increasingly the default. Two short commands to switch.

Lockdown mode separates root from kernel. integrity blocks code modification; confidentiality also blocks reads. Cheap, broad, underused.

Landlock lets an unprivileged process restrict its own filesystem and network access at the kernel level. AppArmor without root, seccomp with semantics.

io_uring gives userspace a submission queue that sidesteps the normal syscall path. It has produced a steady stream of kernel CVEs and routinely bypasses seccomp.

Cloud VMs exposed to the internet with password-only SSH are compromised within hours. This article covers the complete secure access stack: SSH key authentication, TOTP two-factor login, WireGuard VPN as a network-layer gate, and audit logging to track who did what and when.

Every SSH hardening guide starts and ends with the same three changes: disable root login, require key-based authentication, change the default port.

Most Linux hosts resolve DNS in plaintext over UDP port 53. On a stock Ubuntu 24.04 or RHEL 9 system:

Linux kernels ship with defaults optimised for compatibility, not security. On a stock Ubuntu 24.04 or RHEL 9 installation.

Without boot security, an attacker with physical access or console access (BMC, IPMI, cloud serial console) to a Linux system can.

/proc and /sys are virtual filesystems that expose kernel internals, hardware details, and process information to userspace.

auditd is the kernel-level audit system on Linux, it captures syscalls, file access, user commands, and privilege changes that no userspace tool can...

iptables is deprecated. nftables is the replacement in every modern Linux kernel (5.0+).

Without resource limits, a single service, container, or compromised process can consume all available CPU, memory, I/O bandwidth, or PIDs on a host.

SELinux is the most powerful mandatory access control system on Linux, and the most disabled. The result: services have no MAC confinement.

Accurate time is a silent dependency of almost every security control on a Linux system.

Manual OS hardening does not scale. The sysctl settings from Hardening the Linux Kernel Attack Surface with sysctl and Boot...

PAM (Pluggable Authentication Modules) is the authentication foundation on Linux.

The Linux kernel loads modules on demand. When a process requests a capability that is not built into the running kernel (a filesystem type, a...

ubuntu:latest ships with over 200 packages. At any given point, a vulnerability scan with Trivy will report 50 or more CVEs, most of which are in...

AppArmor is the default mandatory access control system on Ubuntu and Debian. It restricts applications to specific file paths, capabilities, and...

systemd provides over 30 security-relevant directives for sandboxing services, yet the vast majority of unit files (including those shipped by...

Default Linux installations mount most filesystems with permissive options. On a stock Ubuntu 24.04 or RHEL 9 system:

HAProxy's defaults are friendly to misconfiguration. The right knobs make it fast, observable, and resistant to common L7 abuse.

Pod egress in a service mesh is a per-Pod decision; egress gateways centralize, audit, and bound it. The pattern that finally makes 'where can my workload reach' answerable.

WireGuard turns the public Internet into an internal network. Three deployment patterns, three different operational models, one cryptographic core.

XDP runs your filter at the network driver level, before the kernel allocates an sk_buff. Drop attacks at line rate on commodity NICs with a few hundred lines of eBPF.

TLS 1.3 still leaks the destination hostname via SNI. ECH closes that gap. Browser support is now wide enough to deploy in production.

Two recent CVE classes weaponize HTTP/2's stream and header model. Mitigation is settings-tweak in NGINX and Envoy, but only if you know which knobs.

QUIC moves TLS into the transport. New attack surface: UDP amplification, 0-RTT replay, connection ID tracking, stream flow-control abuse. Hardening is non-trivial.

AI-powered botnets of compromised IoT and edge devices launch DDoS attacks exceeding 1 terabit per second. These attacks are increasingly used as smokescreens for simultaneous data theft operations. This article covers the multi-layer defensive architecture from edge absorption to origin hardening.

Most production environments run dual-stack (IPv4 and IPv6) whether the team intended it or not. Linux enables IPv6 by default.

gRPC services exposed through API gateways face unique security challenges: gRPC-Web transcoding introduces injection surfaces, metadata headers can carry internal routing information past the edge, and per-method rate limiting requires gRPC-aware configuration.

Most NGINX hardening guides stop at TLS configuration, cipher suites, certificate setup, HSTS.

Rate limiting is the first line of defence against abuse, credential stuffing, API scraping, and denial-of-service attacks.

"It's internal" is the most dangerous phrase in infrastructure security. Internal APIs sit behind the perimeter and receive minimal scrutiny.

Load balancers sit at the most critical point in your infrastructure: every external request passes through them.

Without a centralized API gateway, authentication and authorization logic is duplicated in every backend service. This creates several problems:

TLS misconfiguration remains one of the most common security findings in production infrastructure.

Internal service-to-service traffic in most Kubernetes clusters is plaintext. Once an attacker compromises a single pod, through a container escape,...

L4 load balancers break gRPC multiplexing, sending all streams to a single backend. This article covers L7 balancing with Envoy, client-side balancing with xDS, health check hardening, and connection draining for secure gRPC deployments.

DNS is the most critical single point of failure in any infrastructure, and the least hardened layer for most teams.

A self-managed Web Application Firewall (WAF) with default rules generates dozens of false positives per day.

HTTP request smuggling exploits inconsistencies in how chained HTTP processors (reverse proxies, load balancers, backend servers) parse request...

Security headers are free, server-side controls that instruct browsers to restrict dangerous behaviour.

WebSocket connections start as an HTTP upgrade request and then persist as a long-lived, full-duplex channel.

gRPC services in production frequently run with security configurations that would never be acceptable for HTTP APIs:

Standing CI permissions are a liability. JIT mints production permissions only at deploy time, with explicit approval and short lifetime.

Bots that update dependencies are great until one auto-merges a malicious release. The defaults are safe-ish; the configuration that makes them production-safe is more deliberate.

Four identity types, four very different scope/lifetime/permission models. Pick wrong and you ship the wrong-shaped credential to every CI run for years.

Container-based CI runners share a host kernel. Firecracker and Kata give each job its own kernel and a fresh VM — large blast-radius reduction, modest cost.

OIDC federation between CI and cloud removes long-lived secrets. The trust policies that grant the access are the new attack surface, and most are too loose.

Hand-clicking branch protection rules across 200 repos guarantees drift. Terraform + the github provider + a shared module makes it auditable, reviewable, and reversible.

Most build pipelines run with unrestricted outbound internet. A single compromised dependency exfiltrates secrets, tokens, and source code in seconds.

Attackers no longer need to breach you directly when they can compromise a vendor, open-source library, or managed service provider that you trust. A single poisoned dependency can cascade into thousands of downstream organisations. This article covers the controls that detect and contain supply chain compromise.

Static credentials in CI/CD pipelines are the leading cause of secret sprawl. Teams store long-lived API keys, database passwords, and cloud provider.

SBOM generation is easy, run Syft, get a list of every package in your container image.

Terraform state files contain every secret, IP address, and configuration detail of your infrastructure in plaintext JSON.

Container registries store the most sensitive artifacts in your deployment pipeline.

CI/CD pipeline definitions live alongside application code in Git.

Helm values files control security-critical Kubernetes fields like security contexts, image references, and resource limits. Without schema validation, a single misconfigured value can deploy a privileged container or pull an unscanned image.

CI/CD runners are the most privileged, least monitored components in most infrastructure.

Helm is the dominant package manager for Kubernetes, but most teams install charts without verifying provenance, pass unvalidated values that end up...

Helm charts pulled from public repositories are unsigned, unverified, and executed with whatever permissions their templates request. This article covers OCI-based chart storage, cosign signing and verification, chart mirroring for airgapped environments, and Kyverno policies to enforce signed charts.

Build artifacts pass through multiple stages between source code and production deployment.

GitHub Actions is the most widely used CI/CD platform, but its security model is scattered across dozens of documentation pages.

Dependency confusion and typosquatting attacks exploit the gap between "I declared a dependency" and "I verified the dependency I got." Version pinning...

Two builds from the same source code should produce the same container image. In practice, they almost never do.

GitOps centralizes deployment authority in Git repositories. Tools like ArgoCD and Flux watch Git repositories and reconcile cluster state to match...

Without provenance, you cannot prove where a container image came from, what source code it was built from, or whether the build process was tampered...

An agent's run is a graph of model calls, tool invocations, and decisions. Observability that maps cleanly to that graph is the difference between debugging and guessing.

SynthID, the Aaronson scheme, and lexical watermarks embed signatures in model output. Detection works statistically. None survives heavy editing — useful but bounded.

Manual red-teaming finds gaps once. Continuous pipelines find regressions every model upgrade. The infrastructure exists; most teams haven't wired it up.

Synthetic media is now indistinguishable from camera output. Content Credentials are the practical defense — signed manifests embedded in the file itself.

MCP servers expose tool surfaces to LLM agents. The auth model decides what an agent can do — and most deployments leave it underspecified.

Provider-side prompt caching speeds up applications by 30-90% — and introduces a new attack surface with timing side-channels and poisoning vectors.

Agents with long-term memory survive across sessions. Anything poisoned into that memory persists. A one-shot prompt injection becomes a permanent behavioural change.

A modern virus is not the same as a virus from five years ago. AI-generated payloads observe their environment, profile the host, detect sandboxes, adapt their persistence mechanism to the OS they land on, and modify their C2 communication to blend with normal traffic. Every instance is unique. This article covers how adaptive malware works and the defensive controls that defeat it.

If Anthropic's Mythos can find your vulnerabilities, so can every attacker with API access. The only rational response is to find them first. This article covers how to run systematic AI-powered security assessments across your code, infrastructure-as-code, and runtime configuration.

Generative AI has eliminated every traditional indicator of phishing: perfect grammar, personalised context, cloned executive voices, and real-time video deepfakes. This article covers the defensive controls that work when human judgement alone cannot distinguish real from fake.

Frontier AI models like Anthropic's Mythos find vulnerability classes that traditional scanners miss: logic flaws, implicit trust, hardcoded secrets, configuration drift. The defensive response is not faster patching. It is eliminating these classes before they are discovered.

Large language models memorise portions of their training data. Given the right prompt, a model will reproduce training examples verbatim, including..

Model extraction (model stealing) is an attack where an adversary queries a production ML API systematically to reconstruct a functionally equivalent...

AI agents are being deployed with production tool access: shell execution, kubectl, terraform apply, database queries, API calls.

AI governance in most organisations is a manual process. A model is trained, someone writes a document, a committee meets, approvals are collected...

AI systems introduce a supply chain attack surface that traditional software security does not cover. The three new vectors are.

The EU AI Act entered into force in August 2024, with enforcement timelines staggered through 2027.

MCP servers expose tools that agents invoke. Without fine-grained permissions, every connected agent can call every tool. This article covers least privilege patterns, per-client allowlists, human approval gates, audit logging, multi-tenant isolation, and capability tokens.

Static application security testing (SAST) tools find pattern-based vulnerabilities effectively. Semgrep matches code against rules.

AI agents operate at machine speed, generating 10-100x the audit data of human operators.

MCP supports three transport types: stdio, SSE, and HTTP. Each has distinct security characteristics. This article covers transport-level hardening for all three, including process isolation, TLS, mTLS, CORS, reverse proxy configuration, and rate limiting.

Kubernetes security scanners evaluate resources individually. Tools like kube-bench check node configurations against CIS benchmarks.

LLM jailbreaks are inputs that cause a model to ignore its system prompt, safety training, or usage policies.

AI agents generate infrastructure configurations, database migrations, deployment manifests, and shell commands. It passes a casual review.

The Model Context Protocol (MCP) gives AI agents structured access to tools: filesystem operations, database queries, API calls, shell commands.

Infrastructure-as-Code scanners like Checkov, tflint, and cfn-lint enforce policy through pattern matching.

LLM applications are vulnerable to prompt injection, system prompt leakage, and cross-user context contamination. This article covers system prompt hardening, input sanitisation, output filtering, and context isolation for multi-tenant deployments.

AI systems make decisions that affect people: who gets approved for a loan, whose resume gets shortlisted, which content gets flagged, whose...

AI models are no longer just tools that engineers use to write code. They are becoming direct infrastructure consumers:

Signature-based detection (WAF CRS rules, static Falco rules, antivirus signatures) matches "known bad." AI-generated attacks are polymorphic, every...

Embedding-based retrieval powers RAG pipelines, semantic search, recommendation systems, and classification.

AI models can now discover exploitable vulnerabilities in source code faster than human researchers.

Multi-agent systems are moving from research demos to production deployments. A coordinator agent delegates tasks to specialist agents: one handles...

Deploying LLMs in production introduces infrastructure security challenges: model integrity verification, GPU isolation, runtime sandboxing, API authentication, and safe model updates. This article covers the full inference deployment security stack.

Every security architecture is built on assumptions about what attackers can do, how fast they can do it, and at what scale.

Every production AI model has boundaries: input domains where it performs well, edge cases where it fails, and security properties that constrain how...

AI agents with write access to production systems can execute 100+ infrastructure changes per minute.

The gap between vulnerability disclosure and weaponised exploit used to be measured in weeks.

Membership inference attacks determine whether a specific data record was used to train a model.

AI agents execute tool calls on real infrastructure: writing files, running shell commands, making HTTP requests, modifying databases.

Traditional security scanners operate on pattern matching. They check for known CVEs in dependency trees, match regex patterns for hardcoded secrets,...

Manual security review of infrastructure-as-code takes 2-4 hours per pull request for complex changes.

AI agents need credentials to do useful work: database passwords, API keys, Kubernetes service account tokens, cloud IAM roles.

Traditional incident response assumes failures are binary: the service is up or it is down, the response is correct or it throws an error.

When a security alert fires at 2 AM, the on-call engineer faces an information overload problem.

Per-rule grouping and fingerprint-based dedup get you from 10,000 alerts/day to 200. Correlation across signals is the next jump — to 30 actionable incidents.

What you don't capture, you can't investigate. Forensic readiness is the discipline of designing the logging layer so post-incident you have what you need.

Treat security as a service: define SLIs (detection coverage, MTTD), set SLOs, track burn rate. The same discipline that makes reliability measurable makes security measurable.

If you cannot measure your detection program, you cannot improve it. The metrics that matter, how to compute them, and what they trigger when they shift.

OTel traces capture authorization headers, URL params, internal IDs, and database query strings by default. Without redaction, your traces are an exfiltration target.

SIEM bills double yearly because nobody owns the spend. Cardinality control, retention tiering, and sampling reduce cost 40-70% without losing detection.

Detection logic scattered across SIEM consoles and shell scripts does not scale. Sigma rules in Git, tested in CI, converted to any backend on deploy, do.

The OpenTelemetry Collector processes every trace, metric, and log in your infrastructure. A compromised Collector leaks all observability data.

Most security dashboards are vanity metrics, total alerts this month, pie charts of vulnerability severity, traffic heatmaps that look impressive but.

Distributed tracing is standard for performance debugging, but almost no team uses it for security.

An OTel Collector pipeline with default settings forwards every attribute, header, and trace to your backend with no filtering or authentication.

East-west traffic inside a Kubernetes cluster is a blind spot for most security teams.

Prometheus is deployed in most Kubernetes environments for infrastructure monitoring (CPU, memory, disk, request latency.

Falco monitors syscalls for runtime detection. Tetragon (CNCF/Cilium) goes deeper: it monitors process execution, network connections, and file...

An attacker's first post-compromise action is covering their tracks. On a Linux host, this means deleting /var/log/audit/audit.log, clearing journal..

Container escapes are the highest-impact attack in Kubernetes. A single compromised pod that escapes its container gains access to the underlying...

Kubernetes audit logging at the RequestResponse level captures everything: every API call, every request body, every response payload.

Cryptojacking is the most common post-compromise activity in Kubernetes environments.

Security detection that generates 50+ false positives per day is worse than no detection, it trains the team to ignore alerts.

Certificate expiry is the most common cause of preventable production outages. When a TLS certificate expires, HTTPS connections fail, mTLS...

Detection without documented response is security theatre. Most teams have alerts that fire at 3 AM, but no written procedure for what the on-call...

Self-managed log infrastructure is one of the highest operational costs for small-to-medium teams.

Linux audit logs are the ground truth for security investigation. auditd captures kernel-level events that no userspace tool can see: file access by...

Security-side WASM (auth filters, policy engines, MCP plugins) must be sub-millisecond to deploy at request rate. Pre-compilation and snapshotting get you there.

WASM lets you ship logic to constrained devices without firmware updates. The runtime, the trust model, and the OTA pipeline all need careful design.

Plugin systems built on WASM have a recurring shape. Threat-modeling that shape catches the structural mistakes before deployment.

Edge runtimes execute untrusted customer code in shared processes. The hardening contract is the platform's, but the customer code's behavior decides the blast radius.

WASM plugins run inline in the data path. A misconfigured plugin can exhaust memory, leak tenant data, or crash the proxy. The defaults need explicit caps.

ngx_wasm_module brings the proxy-wasm protocol to NGINX. Plugin authoring is similar to Envoy, but the worker model and hardening surface differ.

WASM is the easy case for reproducibility — no dynamic linking, no runtime variance. Most teams still ship non-reproducible builds. The fix is small.

WASI HTTP servers are a clean platform-neutral pattern. The hardening is at the application layer — body limits, header allowlists, response shaping, and panic semantics.

Preview 2 replaces Preview 1's coarse imports with explicit, scoped, capability-passing interfaces. The security story is the actual reason to migrate.

wasi:sockets/tcp and wasi:sockets/udp give WASM modules network access. The capability model is fine-grained — most embedders use it as a coarse on/off switch.

Running AI inference inside WASM is a new deployment pattern with novel isolation properties. The threat model differs from GPU-served inference.

When you compose multiple components, every wire is a capability decision. The security story of a composed application lives in the WIT between components.

Databases are growing WASM extension points. The threat model spans both WASM-runtime escape and database-internal lateral access — different from container UDFs.

Running many tenants' WASM modules in one runtime is the hard case. Per-tenant fairness, isolation guarantees, and the failure modes that violate both.

WASM modules ride OCI registries the same as containers. The supply-chain hygiene story is the same — and most orgs do not apply it to .wasm artifacts.

WASM on Kubernetes via runwasi and containerd shims runs alongside containers but with a different escape surface, different RBAC implications, and different supply-chain controls.

Scanning .wasm artifacts is different from scanning containers — no rootfs, no package manager. The dependency graph is in the bytecode.

Wasmtime's defaults are friendly, not safe. Untrusted modules need explicit limits on CPU, memory, syscall surface, and filesystem access.

Wazero is the pure-Go WASM runtime used by Tetragon, Cilium, k6, Trivy, and dapr. The defaults are friendly; production deployments need explicit caps.