Agentic AI security for apps that ship fast.
SecureNow blocks bad traffic at the door, watches what happens inside your app, and gives AI agents the evidence to take care of bots, attacks, suspicious users, and production failures in plain English.
Vibecoders
You ship with Cursor, Claude, and speed. SecureNow gives those agents real production context so they can find failures, spot attacks, and help fix what shipped too fast.
SaaS founders
You need uptime, trust, and fewer surprise bills. SecureNow blocks bot waste, catches silent bugs, and explains security incidents before customers do.
SMB teams
You do not have a SOC. You still have login pages, payments, customer data, and bots knocking every day. SecureNow adds the missing security layer without enterprise overhead.
Fast teams are exposed from three sides at once.
The product is moving faster, the internet is noisier, and attackers do not wait until you can afford a security hire. SecureNow is built for that awkward middle: real production risk, small-team bandwidth.
Bots hit you before customers do
Scrapers, scanners, credential stuffers, fake signups, and LLM crawlers burn CPU, create noise, and inflate bills before your product even has a security process.
AI-built features create unknown risk
Fast shipping means more unreviewed endpoints, new dependencies, missing validation, and auth edges. You need runtime evidence, not a weekly checklist.
Normal tools see symptoms, not attacks
APM tells you a route is slow. Error tracking says a 500 happened. Neither explains whether a bot, a bad deploy, or an attacker caused the mess.
Enterprise security is too heavy
Classic WAFs, SIEMs, and SOC workflows assume dedicated staff, budgets, and infra control. Small teams need protection that starts in minutes and grows up later.
One agentic security loop: prevent, observe, understand, respond.
A firewall alone blocks noise. Monitoring alone explains symptoms. SecureNow combines both, then gives AI agents the command surface to investigate, decide, and prepare action.
$ securenow forensics "who is attacking signup?"
Found credential stuffing on /api/signup and /api/login.
4,812 failed attempts, 92% from flagged subnets, 14 user agents, no cookies.
Recommended: block 38 IPs, alert Slack, watch related endpoints for 24h.
Prevent
Firewall at the door
Block 500k+ known-bad IPs, scanners, credential stuffers, and bot traffic before your handlers run. Legitimate Googlebot, GPTBot, ClaudeBot, and other trusted crawlers stay welcome.
- Free firewall-only mode
- Hourly blocklist refresh
- HTTP, TCP, kernel, and cloud WAF layers
Observe
Full app telemetry
Capture traces, logs, endpoints, status codes, latency, IPs, request bodies, and multipart metadata with redaction so every security question has evidence behind it.
- OpenTelemetry-native
- Logs and traces together
- Next.js, Nuxt, Vite, and Node framework support
Understand
Agentic AI investigates
Chat with your app's logs and traces like an expert analyst. The agent follows evidence across IP intelligence, user journeys, payloads, errors, slow spans, and prior alerts until it can explain intent and impact.
- ChatGPT-style forensics
- Trace-level root cause
- Claude/Cursor/Codex MCP tools
Respond
Block, alert, and audit
Block an IP, create an alert, review false positives, or send a Slack/email/webhook update. Every action stays visible and reversible from CLI or dashboard.
- Manual or automated blocking
- Scoped trusted IPs and allowlists
- Human-review workflows
Detect the bad traffic
Spot spikes in probes, bot patterns, payload anomalies, and blocked IP activity before they blend into normal logs.
Analyze the full story
Pivot from an IP to traces, bodies, endpoints, user journey, and AI investigation notes in one workflow.
Defend without waiting
Block, allowlist, review false positives, and automate alerts from the same command surface your agents can use.
Give your security agent eyes on production.
Claude, Cursor, Codex, and your scripts can only help when they have live facts. SecureNow turns app traffic into structured CLI and dashboard evidence an agent can use.
Is anyone attacking signup right now?
A founder asks from the dashboard, terminal, Cursor, or Claude Code.
SecureNow checks traces, logs, bodies, and IP intelligence
The agent finds failed logins, suspicious user agents, blocked IPDB hits, and repeated payload patterns.
Credential-stuffing botnet, 14 subnets, signup targeted
It explains the blast radius, affected paths, confidence, and recommended response.
Firewall rule applied, alert sent, report saved
The bad sources are blocked, the team gets the summary, and the investigation is auditable.
Talk to your app's logs and traces like an expert incident responder.
This is the product moment: a ChatGPT-style investigation surface where you ask what happened, then SecureNow reads the traces, logs, bodies, alerts, IP history, and response state for you. The answer is not a generic summary. It is a defensible incident report with evidence and actions.
Traces reviewed
18,432
HTTP spans, route handlers, auth-service calls, and database lookups across the attack window.
Logs correlated
6,908
Auth failures, rate-limit decisions, session creation logs, and application errors tied back to trace IDs.
Payloads inspected
412
Redacted request bodies showed repeated email/password tuples and identical automation fingerprints.
Actions prepared
5
Scoped block, rate limit, Slack alert, session revocation, and a saved alert rule for recurrence.
SecureNow AI incident chat
signup-api production · live evidence attached
Verdict: active credential-stuffing plus account enumeration campaign.
Confidence is 97%. The spike is not a deploy regression: the same 82 source IPs hit /api/signup and /api/login with no UI navigation, rotating user agents, and repeated 401 to 200 transitions.
- Attack window: 09:41-10:18 UTC
- Primary paths: POST /api/signup, POST /api/login, GET /api/users/check-email
- Top source: 185.220.101.34, SecureNow IPDB 100%, Tor exit node
Timeline reconstructed from traces and logs.
The first phase tested whether email addresses existed. The second phase attempted passwords against known accounts. The third phase created sessions for 17 accounts, then stopped before profile export or payment routes.
- 09:41 - email oracle probing starts, 3,104 requests, mostly 200/404
- 09:48 - login burst begins, 12,772 requests, auth-service latency rises 4.8x
- 09:56 - 17 successful sessions created, all from new device fingerprints
- 10:03 - rate-limit rule starts returning 429 and campaign shifts to /api/signup
Impact appears contained to account access, not data export.
I found 17 successful logins, but no downstream calls to /api/profile/export, /api/payments, /api/admin, or GraphQL customer-data queries from those sessions. Three accounts have high risk because they reached /api/account.
- Sample trace: tr_7f2a91c4 links login, session creation, and the only account route hit
- No matching object-store downloads, invoice reads, or privileged route access
- Recommended: revoke 17 sessions and force password reset on the 3 high-risk accounts
Shared infrastructure and payload fingerprints tie the sources together.
The IPs are spread across providers, but the requests reuse the same body shape, timing jitter, and header order. Several IPs also appear in prior scanner alerts against /graphql and /.env.
- Payload fingerprint: email, password, rememberMe fields in identical order
- User agents: headless Chrome variants with missing Accept-Language
- Network: 61% from two ASNs, 22 IPs already seen in scanner alerts this week
Ready for review: contain now, then harden the signal.
I prepared a scoped response that stops the campaign without blocking normal signup traffic. You can approve each item, export the report, or turn the detection into a permanent alert rule.
- Block 82 IPs for 24h and watch their /24s in monitor mode
- Rate-limit /api/login by IP and email hash: 5 attempts per minute
- Revoke 17 sessions, force reset on 3 accounts, notify Slack #security
- Create alert: more than 10 failed logins followed by 1 success from a new device
Executive summary
Credential-stuffing campaign targeted signup and login. No evidence of bulk data export. Containment actions are ready with low false-positive risk.
Evidence chain
Every finding links back to raw traces, correlated logs, redacted request bodies, IP intelligence, and the specific alert rule that first surfaced the anomaly.
Next best actions
Approve scoped blocking, session revocation, password resets, a temporary rate-limit, and a saved recurrence detector for the same attack pattern.
We are positioned where the problem actually happens: inside the app.
Edge products see the perimeter. Error tools see exceptions. SecureNow sees the request, the trace, the logs, the payload, the IP reputation, and the response action in one place.
One install covers prevention and visibility
Most teams bolt a WAF to an APM and still have to stitch evidence by hand. SecureNow puts the firewall, telemetry, and forensics on the same app-aware data path.
Built for agentic development
Every CLI command can return structured output, so Claude, Cursor, Codex, and your scripts can investigate and act without guessing from screenshots.
App-layer context beats edge-only signals
An edge firewall sees IPs and paths. SecureNow also sees traces, request bodies, users, errors, and latency, so the AI can explain intent and impact.
Good bots are treated differently
You can block malicious automation without accidentally hiding from search engines and AI discovery crawlers that help customers find your product.
Plain-English security for small teams
You do not need to know what a credential-stuffing graph looks like. Ask the question, get the answer, then choose the response.
Upgrade path is natural
Start with the free firewall. When the company needs deeper monitoring, the same package turns on traces, logs, body capture, AI forensics, alerts, and workflows.
Start narrow. Grow into full coverage.
Use firewall-only mode when you just need bot blocking. Use full mode when you want the monitoring and AI agent layer. Both are designed for the same fast setup path.
Free firewall first
Block bad IPs and enforce route limits with no tracing and no telemetry pipeline.
npm install securenow@latest
npx securenow login
node -r securenow/firewall-only app.jsFull security monitoring
Turn on firewall, traces, logs, console capture, body and multipart evidence, and AI-ready context.
npm install securenow@latest
npx securenow login
node -r securenow/register app.jsLet an AI agent wire it
Use the onboarding prompt to pick the app, write .securenow/credentials.json, update framework files, and verify the setup.
npx securenow login && npx securenow initWhy one platform beats a pile of point tools.
| Need | Typical point solution | SecureNow |
|---|---|---|
| Block bot traffic | Separate WAF, DNS move, or edge config | Node preload firewall, trial start, trusted crawler allowlist |
| Understand an incident | APM, error tracking, and logs in separate tools | ChatGPT-style investigation across traces, logs, IP intelligence, bodies, alerts, and response actions |
| Let AI help | Screenshots and manual prompts | Structured CLI output and dashboard actions agents can use |
| Operate as a small team | Security process designed for enterprise staff | Plain-English workflows that start with one npm install |
Questions before you drop it in.
Is this for developers or security teams?
Both. Developers get a firewall, traces, logs, and plain-English answers from their production app. Founders and SMB operators get security coverage without hiring a full SOC team. Security teams can still use the same data, CLI, and dashboard when the company grows.
Can I start with only the free firewall?
Yes. The firewall-only preload blocks 500k+ known-bad IPs and keeps legitimate SEO and AI crawlers allowed. When you want full traces, logs, request-body forensics, alerts, and AI investigations, you can switch to the full SecureNow preload with the same account.
What does the AI agent actually do?
It acts like an agentic security teammate: you chat with your app's logs, traces, request bodies, IP intelligence, and alerts in plain English. The agent correlates activity by IP, user, path, status code, payload, and trace, then explains what happened, what was affected, and what response action is ready for review.
Do I need Cloudflare, AWS WAF, or a load balancer change?
No. SecureNow runs inside your Node.js app. The firewall is loaded with a Node preload flag, and full monitoring uses the same runtime integration. No DNS move, edge account, load balancer rule, or middleware refactor is required.
Will this slow down my app?
Firewall-only mode uses an in-memory IP lookup and is designed for sub-millisecond checks. Full monitoring exports telemetry asynchronously and fails open, so SecureNow does not block your response path if the backend is unreachable.
Put agentic AI security on your app today.
Start with the free firewall, then turn on the full security monitoring and investigation layer when you want deeper answers.