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AI-Powered Security Operations — Trusted by 60+ enterprises

Secure.
Automated.
Intelligent.

anyops.ai unifies AI/ML, DevOps, DevSecOps, and Cybersecurity into a single cohesive practice — helping regulated industries ship faster, safer, and smarter.

Schedule a Strategy Call FDE Program →
Aligned: ISO 27001 SOC 2 HIPAA PCI-DSS NIST CSF
60+ Enterprise clients
98% Retention rate
3× Deploy velocity
0 Breaches · 14 mo
98% Client retention rate
60+ Enterprise deployments
45% Avg. MTTR reduction
3x Faster deployment velocity
Integrates natively with industry-leading platforms
Amazon Web Services
GitHub
Kubernetes
HashiCorp Vault
Splunk SIEM
CrowdStrike XDR
Okta IAM
Google Cloud
Microsoft Azure
SIGNATURE PROGRAM

Forward Deployment
Engineering

Senior engineers embedded directly inside your organization — deploying production infrastructure, integrating security tooling, and training your team by doing. They ship working software, not slide decks. Zero vendor lock-in on exit.

Explore FDE Program Request an Engagement →
2–12 Week engagements
100% Embedded on-site or remote
Zero Vendor lock-in on exit
Ships production infrastructure from day one
Deploys inside your security perimeter — data never leaves
Pairs directly with your engineers — teaches by doing
Full runbooks & ADRs handed over on exit
AI & Machine Learning

Intelligent Systems That Learn, Adapt, and Protect

From strategy to production deployment, we design and operationalize machine learning systems that generate real business value — and integrate natively with your security posture.

47+ Production ML models shipped for enterprise clients
94.3% Median model confidence maintained in production
3–8× Earlier incident detection vs. threshold-based alerting
<60 days From roadmap sign-off to first model in production

AI Strategy & Roadmap

Most organizations have AI ambitions but lack a credible path to production. We bridge that gap — assessing your data infrastructure, talent, and tooling against your strategic goals to produce a phased, budget-conscious roadmap that actually gets executed.

  • Data maturity & infrastructure readiness audit
  • AI use-case prioritization by ROI and feasibility score
  • Build-vs-buy analysis across model vendors and open-source
  • AI governance, ethics, and risk management framework
  • Executive alignment workshops and board-ready presentations
Typical outcome: 6–18 month phased AI roadmap with prioritized use cases and clear ownership.
What the phased roadmap looks like
P1
FoundationsWeeks 1–6

Data audit, governance baseline, and 1–2 quick-win automations that prove value early.

P2
Pilot & ProveMonths 2–6

Two to three prioritized use cases built to production — measured against agreed ROI baselines.

P3
Scale & EmbedMonths 6–18

Platformize shared ML infrastructure, upskill internal teams, and expand the use-case portfolio.

AI Governance Data Strategy ROI Modeling

ML Model Development & MLOps

We engineer machine learning systems end-to-end — from raw data pipelines and feature engineering through model training, evaluation, and production deployment — with the operational discipline that keeps models reliable months after go-live.

  • Custom model development: supervised, unsupervised, and LLM fine-tuning
  • Production-grade feature stores with point-in-time correctness
  • Automated retraining pipelines triggered by data or performance drift
  • Model versioning, A/B testing, and shadow deployment frameworks
  • Explainability and bias auditing for regulated industries
Typical outcome: Model deployment lead time reduced from weeks to hours with full audit trails.
PyTorch / TensorFlow MLflow Kubeflow Vertex AI

Predictive Analytics & Anomaly Detection

Reactive monitoring catches problems after they hurt you. Our predictive analytics practice builds models that learn the normal behavior of your systems and people — surfacing deviations, forecasting failures, and flagging threats before they reach production or breach data.

  • Time-series forecasting for infrastructure capacity and business KPIs
  • Unsupervised anomaly detection across logs, metrics, and network flows
  • Real-time scoring pipelines with sub-100ms inference latency
  • Security-focused UEBA: detecting lateral movement and data exfiltration
  • Seasonality-aware models resilient to traffic spikes and release cycles
Typical outcome: 40–70% reduction in false-positive alerts; incidents caught 3–8× earlier.
Isolation Forest LSTM / Transformer Apache Flink Kafka

AI-Powered Observability Dashboards

Modern distributed systems produce more telemetry than humans can read. We build intelligence on top of your existing observability stack — unifying metrics, logs, and traces into a single pane, then layering AI to turn raw signals into clear, actionable ops decisions.

  • Unified telemetry ingestion across cloud, containers, and on-prem (OpenTelemetry)
  • AI-generated incident summaries with probable root-cause ranking
  • Predictive alerting: fires before thresholds breach, not after
  • Natural-language query interface — ask your ops data plain questions
  • Cost attribution and waste surfacing across multi-cloud environments
Typical outcome: MTTR reduced by 45%; on-call engineer time cut by 30% within 90 days.
OpenTelemetry Grafana Prometheus Elastic

LLM & Generative AI Engineering

We take generative AI past the demo stage — building retrieval-augmented systems grounded in your private data, hardened against prompt injection, and measured against evaluation suites before anything reaches a customer or an auditor.

  • RAG architecture over your private data — vector + hybrid search
  • LLM fine-tuning and prompt optimization with regression eval suites
  • Guardrails: prompt-injection defense, PII redaction, output filtering
  • Agent workflows with human-in-the-loop approval gates
  • Cost & latency engineering: caching, routing, model right-sizing
Typical outcome: grounded answer accuracy above 95% with hallucination rate under 2% on domain benchmarks.
Claude / Bedrock RAG pgvector / Pinecone Eval Harness

Production ML Reference Pipeline

Every model we ship runs on this backbone — versioned, monitored, and auditable end to end.

Data Sources
Streams, lakes & warehouses
Feature Store
Point-in-time correct
Train & Evaluate
Tracked experiments
Model Registry
Versioned & signed
Serving
CI/CD & canary rollout
Drift Monitoring
Auto-retrain triggers

Responsible AI, Built In

Governance isn’t an afterthought — every engagement ships with the controls regulators and boards expect.

NIST AI RMF

Risk management framework mapped to every use case — govern, map, measure, manage.

EU AI Act

Risk-tier classification and conformity-assessment readiness for EU-exposed systems.

ISO/IEC 42001

AI management system alignment — policies, roles, and lifecycle controls documented.

Model Cards & Bias Audits

Documented lineage, explainability reports, and human-oversight gates for every model.

How We Deliver AI — From Idea to Operations

011–2 wks
Assess

Data readiness audit, use-case ROI scoring, and model risk review.

022–4 wks
Prototype

Offline model on your data — success metrics agreed before build starts.

033–6 wks
Productionize

CI/CD for models, monitoring, rollback paths, and full audit trails.

042–3 wks
Integrate

Wired into your applications, SIEM, and decision workflows.

05Ongoing
Operate

Drift detection, automated retraining, and quarterly value reviews.

Forward Deployment Engineering

Your Environment. Our Engineers.
Zero Friction.

Senior engineers embedded directly inside your organization — deploying, configuring, and iterating on anyops.ai capabilities within your infrastructure and security perimeter. They ship working software, not slide decks.

Embedded on-site or remote
Ships working software, not decks
Full knowledge transfer on exit
Zero vendor lock-in
120+ FDE engagements delivered across 14 regulated industries
9.2/10 Median client engagement score at handover
87% Of clients extend or return for a second engagement
150+ Engineers trained through the FDE academy tracks
For Organizations

Deploy Faster.
Secure Deeper.

Embed senior anyops.ai engineers directly in your team for 2–12 weeks. They ship production-grade infrastructure, integrate security tooling, and leave your engineers fully trained to own everything built.

  • Production deliverables from day one
  • Deploys inside your security perimeter
  • Full compliance evidence wiring included
  • Zero vendor lock-in on exit
Request an FDE Engagement →
For Engineers & Students

Build Real Systems.
Accelerate Your Career.

Work alongside senior FDEs on live enterprise deployments — not simulations. Build a portfolio of production infrastructure that proves you can ship, mentored by engineers with 8+ years of real-world experience.

  • Real Fortune 500 & Series C production work
  • 1-on-1 senior engineer mentorship
  • Structured path: student → junior → senior
  • Internship-to-hire track available
Apply for FDE Training Track →

What is a Forward Deployment Engineer?

Borrowed from the world's most demanding software deployments, the FDE model places senior engineers on-site or embedded remotely with your team for the duration of an engagement. They are simultaneously product experts, systems integrators, and trusted technical advisors — capable of standing up complex infrastructure, customizing pipelines to your exact environment, and training your engineers in real time.

Unlike traditional consulting, FDEs do not hand off decks. They ship working software inside your systems, iterate based on your feedback daily, and leave your team with full ownership of everything they build.

2–12Week engagements
100%On-site or embedded remote
0Vendor lock-in on exit
01

Discovery Sprint

FDE embeds with your team for 3–5 days to audit your infrastructure, toolchain, and current pain points. Outputs a prioritized technical execution plan.

02

Deployment & Integration

Live configuration of pipelines, security tooling, ML infrastructure, or observability stack inside your environment. Daily standups. Weekly demos to stakeholders.

03

Iteration & Hardening

Rapid feedback loops with your engineers. Edge cases addressed. Security hardened. Performance tuned. Documentation written alongside the build — not after.

04

Knowledge Transfer

Structured handover to your internal team: runbooks, architecture decision records, and live training sessions. Your engineers own it from day one.

What Ships — Week by Week

Every engagement is time-boxed with defined outputs. Here's what a typical 8-week implementation looks like.

Week 1
Discovery & Environment Audit
  • Infrastructure inventory & dependency map
  • Security gap analysis against SOC 2 / ISO 27001
  • Toolchain assessment & integration plan
  • Stakeholder alignment & prioritized backlog
Deliverable Technical execution plan + risk register
Weeks 2–3
Foundation Build — Infrastructure & Pipelines
  • Terraform modules for your cloud (AWS / Azure / GCP)
  • Kubernetes cluster bootstrapped with hardened defaults
  • CI/CD pipeline with SAST + secrets scanning gates
  • Secrets management (Vault / KMS) wired to pipelines
Deliverable Fully working IaC + pipeline — deployed to staging
Weeks 4–5
Security Layer & Observability
  • SIEM integration & alert tuning (Splunk / Sentinel / Elastic)
  • CSPM configured across all cloud accounts
  • Policy-as-code gates (OPA / Rego) — SOC 2 controls mapped
  • AI anomaly detection model deployed on your telemetry
Deliverable Live security dashboard + automated compliance evidence
Weeks 6–7
Hardening, Tuning & Team Training
  • Production cut-over with zero-downtime deployment
  • Incident response playbooks authored & tested
  • Your engineers paired and owning every component
  • Performance tuning + cost optimization pass
Deliverable Production-live infrastructure + trained internal team
Week 8
Handover & Ongoing Support Setup
  • Architecture Decision Records (ADRs) finalized
  • Runbooks tested in live incident simulation
  • 30-day post-engagement office hours included
  • Optional retainer for ongoing advisory
Deliverable Full documentation package — your team owns everything

FDE vs. the Alternatives

Most organizations choose between slow, expensive, or fragile. FDE is none of those.

Big 4 / SI Consulting
Hire In-House
anyops.ai FDE
Time to first deliverable
6–12 weeks (kick-off & planning)
4–9 months to hire & ramp
Week 2 — production builds in your env
Typical cost (6 months)
$400K–$900K
$180K salary + $60K overhead
Fixed engagement — no bloat
What you actually get
Slide decks & recommendations
1 person, 1 skill set
Working production infrastructure
Knowledge transfer
Dependency on vendor to operate
Limited — one person’s knowledge
Full runbooks, ADRs, paired training
Data & security risk
Consultants access your data off-site
Internal — but single point of failure
Deploys inside your perimeter — data stays
Vendor lock-in on exit
High — custom tooling & ongoing licenses
Risk of key-person dependency
Zero — all open standards, your team owns it
2× Faster time-to-production vs. traditional consulting
40% Average cloud cost reduction after FDE infrastructure pass
<5 days SOC 2 audit prep time after FDE compliance wiring
100% Of engagements ship production deliverables before week 3

Engagement Models & Team Composition

Scale the engagement to the problem — from a single embedded expert to a full platform pod. Every model is fixed-scope, fixed-price, with weekly executive reporting.

Solo FDE
1 senior engineer
Duration2–6 weeks
CadenceDaily standups · weekly demo
Best forSingle-system deployment or integration
  • One focused deliverable — e.g. CI/CD security gates or SIEM tuning
  • Direct pairing with your existing team
  • Runbook + ADR handover included
Most common
FDE Pod
2–3 engineers · 1 lead
Duration6–10 weeks
CadenceDaily standups · weekly exec report
Best forFull platform or security program build-out
  • Parallel workstreams — infra, security, and ML tracks
  • Compliance evidence wiring (SOC 2 / ISO 27001)
  • 30-day post-engagement office hours
Embedded Platform Team
4+ engineers · lead + architect
Duration10–12+ weeks
CadenceEmbedded in your sprint rituals
Best forEnterprise transformation or migration
  • Multi-cloud / multi-region delivery capacity
  • Dedicated architect for governance & design authority
  • Structured hiring support to backfill your own team

Infrastructure Deployment

Stand up production-grade Kubernetes clusters, Terraform-managed cloud infrastructure, or GitOps pipelines directly inside your AWS, Azure, or GCP account — configured to your compliance and networking requirements.

  • Cloud-native IaC from day one
  • Hardened network policies & IAM
  • Fully auditable state management

AI & ML Integration

Deploy anomaly detection, risk scoring, or UEBA models inside your data environment. Connect to your existing SIEM, data warehouse, or event streams — no data leaves your perimeter.

  • On-prem and VPC-isolated deployments
  • Custom model fine-tuning on your data
  • Real-time inference pipeline setup

Security Pipeline Enablement

Embed SAST, DAST, secrets scanning, and policy-as-code gates into your existing CI/CD workflows. Configure severity thresholds, notification routing, and break-the-build rules tailored to your risk appetite.

  • Zero disruption to existing developer workflow
  • Incremental rollout & tuning
  • SOC 2 / ISO 27001 evidence wiring

Team Enablement & Coaching

FDEs pair directly with your engineers — not just setting up systems but teaching the underlying principles. Secure coding practices, IaC patterns, incident response playbooks, and MLOps workflows are transferred by doing, not presenting.

  • Hands-on pairing sessions
  • Custom runbooks & ADRs
  • Post-engagement office hours

The Stack We Deploy

Production-hardened, open-standard tooling — chosen to fit your environment, never to create lock-in.

Cloud & IaC
AWSAzureGCPTerraformPulumiCloudFormation
Containers & Orchestration
KubernetesEKS / AKS / GKEHelmArgoCDIstio
CI/CD & Supply Chain
GitHub ActionsGitLab CIJenkinsSLSASigstoreTrivy
Security & Compliance
SplunkMicrosoft SentinelElasticVaultOPA / RegoCSPM
Data & ML
SageMakerBedrockMLflowKafkadbtFeature Stores
Observability
PrometheusGrafanaOpenTelemetryDatadogPagerDuty
Training Academy

anyops.ai Engineering Tracks

Structured programs for developers at every level — from students shipping their first pipeline to senior engineers mastering enterprise security architecture. All tracks include real production work.

DevSecOps Engineering
12 weeks · All levels
Student Junior Mid-Level
  • CI/CD pipeline design with security gates
  • SAST, DAST & SCA integration
  • Policy-as-code with OPA / Rego
  • Kubernetes security hardening
  • Secrets management — Vault & KMS
↗ Ship a production DevSecOps pipeline for a real client before graduation.
AI & ML Engineering
16 weeks · Junior to Senior
Junior Mid-Level Senior
  • MLOps pipelines & feature stores
  • Model training, evaluation & deployment
  • LLM fine-tuning & RAG architecture
  • Anomaly detection & UEBA models
  • Production inference optimization
↗ Deploy a production ML model inside a regulated industry environment.
Cloud Security Architecture
10 weeks · Mid to Senior
Mid-Level Senior
  • Zero-Trust architecture design
  • SIEM / SOAR configuration & tuning
  • Cloud security posture management
  • Identity & access management (IAM/PAM)
  • Incident response & digital forensics
↗ Architect Zero-Trust controls for a cloud-native enterprise environment.

The FDE Career Ladder

A defined competency path from first pipeline to owning enterprise engagements — every level maps to real production responsibility, not time served.

L0
Student FDE
0–1 yrs
  • Ships first production pipeline under mentorship
  • Earns CKA or Terraform Associate certification
  • Pairs daily with senior engineers on live client work
L1
Junior FDE
1–3 yrs
  • Owns a single workstream inside an FDE pod
  • Runs client standups and demo sessions
  • Hardens CI/CD and IaC without supervision
L2
Mid-Level FDE
3–6 yrs
  • Leads solo engagements end-to-end
  • Designs security & platform architecture
  • Mentors juniors and students on-site
L3
Senior / Lead FDE
6+ yrs
  • Runs multi-pod enterprise programs
  • Owns executive relationships & steering
  • Sets technical strategy and governance authority
New Grad & Student FDE Program

Start Your Career on
Real Production Systems

No toy projects. No simulated environments. anyops.ai’s student FDE track places you alongside senior engineers on live enterprise deployments — building the portfolio that proves you can ship.

01
Real enterprise production work Deploy infrastructure for Fortune 500 and Series C companies — work that goes live
02
1-on-1 senior mentorship Paired with an FDE who has 8+ years of production experience across regulated industries
03
Certifications included CKA, AWS Solutions Architect, or HashiCorp Terraform Associate — your choice
04
Hire track for top performers High-performing graduates are offered full-time FDE positions at competitive salaries
Program at a Glance
Rolling applications — reviewed within 5 business days
Duration 10–16 weeks
Format Remote & on-site rotations
Stipend Paid — competitive rate
Prerequisites CS degree or bootcamp + 1 project
Cohort size 8–12 engineers per cohort
Next cohort Applications open now
Apply for the FDE Student Track

We respond to every applicant — no ghosting, no automated rejections.

Industries We Transform

Regulated industries are our core — we understand the compliance obligations, data residency constraints, and risk profiles that generalist consultants don't.

Financial Services
PCI-DSS SOC 2 FedRAMP

Zero-Trust deployments for banks, fintechs, and insurance carriers. Fraud ML models deployed on-prem. Audit evidence automated for examiners.

Healthcare & Life Sciences
HIPAA HITRUST SOC 2

PHI-safe ML pipelines deployed inside VPC. HIPAA-compliant CI/CD with automated BAA management. Zero PHI leaves your environment.

Government & Defense
NIST CSF CMMC FedRAMP

Air-gapped and GovCloud deployments for federal agencies and contractors. CMMC Level 2–3 preparation. NIST 800-53 control automation.

Series B–D Startups
SOC 2 ISO 27001 Scale-ready

Enterprise-grade infrastructure for fast-growing companies without a 20-person platform team. Ship your SOC 2 audit in 60 days, not 9 months.

Manufacturing & OT
IEC 62443 NIST CSF IT/OT

OT/IT network segmentation, anomaly detection on industrial telemetry, and secure remote access for plant floor systems — without production downtime.

SaaS & Cloud-Native
Multi-tenant SOC 2 GDPR

Multi-tenant security architecture, supply-chain hardening, and automated GDPR/CCPA evidence collection for SaaS companies under enterprise procurement scrutiny.

Our Production Guarantee

If your FDE engagement does not ship working, production-grade code inside your environment by the end of week 2, we extend the engagement at no additional cost — no questions asked. We stand behind output, not effort.

Hold Us to It →

What We Need From You to Start

FDE engagements are deliberately low-burden on your organization — four things, and we handle the rest.

1
An executive sponsor

One accountable stakeholder for a 30-minute weekly steering check-in — that’s the entire meeting load.

2
Environment access on day one

Least-privilege, time-boxed credentials that you issue and can revoke at any moment.

3
1–2 engineers for pairing

The people who will own everything after handover — they learn by building alongside the FDE.

4
A definition of done

We co-write measurable success criteria before work begins — the engagement is scored against them.

Frequently Asked Questions

The questions security and engineering leaders ask before an engagement.

How do FDEs access our environment securely?

Your rules, your perimeter. FDEs work under your IAM policies with least-privilege, time-boxed credentials that you issue and can revoke at any time. All work happens inside your cloud accounts and repositories — no code or data is copied out. NDAs and background checks are standard, and we can operate under your clearance requirements.

Who owns the code and infrastructure that gets built?

You do — 100%. Everything is committed to your repositories, under your organization, from day one. We deploy open standards (Terraform, Kubernetes, OPA) specifically so nothing depends on us after exit.

How fast can an engagement start?

Scoping call within 48 hours, engagement letter within a week, and an FDE embedded within 2–3 weeks of signature. For urgent, incident-driven work we maintain a rapid-response bench that can start in days.

Do FDEs work on-site or remote?

Both. Most engagements run embedded-remote inside your Slack/Teams, VPN, and sprint rituals, with on-site weeks at kickoff and production cut-over. Fully on-site engagements are available for air-gapped or classified environments.

How is pricing structured?

Fixed price per engagement, scoped against the week-by-week deliverables above — no hourly billing, no scope creep. If week-2 production delivery slips, the engagement extends at our cost under the Production Guarantee.

What happens after the engagement ends?

Every engagement closes with runbooks, Architecture Decision Records, and live handover training, plus 30 days of office hours. Optional advisory retainers are available — but our exit metric is your team operating everything without us.

Ready to deploy faster with an embedded expert?

Tell us your environment, timeline, and goals — we'll match you with the right FDE profile within 48 hours.

Request an FDE Engagement
Quantum Computing

Prepare for the Quantum Era.
Protect What You Have Today.

Quantum computers will break today's RSA and ECC encryption within the decade. We help enterprises assess their cryptographic exposure, migrate to NIST post-quantum standards, and harness quantum-enhanced ML — before the threat window closes.

Post-Quantum Cryptography
Quantum Risk Assessment
Quantum ML Acceleration
Crypto-Agile Architecture
NIST PQC Standards
2030 NIST target to deprecate classical RSA / ECC cryptography
4 NIST PQC standards we implement — ML-KEM, ML-DSA, SLH-DSA, FN-DSA
100% Cryptographic asset inventory coverage in assessment phase
3–6 mo Typical timeline for a crypto-agile migration pilot

Post-Quantum Cryptography Migration

Harvest-now-decrypt-later attacks are already underway. We inventory your cryptographic assets and migrate RSA, ECC, and DH to NIST-standardized post-quantum algorithms before adversaries gain quantum capability.

  • Cryptographic inventory & dependency mapping
  • Migration to CRYSTALS-Kyber & CRYSTALS-Dilithium
  • TLS 1.3 + hybrid PQC cipher suite deployment
  • Certificate lifecycle management for PQC PKI
↗Typical outcome: Full PQC migration roadmap delivered in 4 weeks with zero service disruption.
CRYSTALS-KyberNIST FIPS 203TLS 1.3

Quantum Risk Assessment

Before you can defend against quantum threats you need to understand your exposure. We audit all systems to identify cryptographic assets at risk and prioritize remediation by business impact and data sensitivity.

  • Crypto-agility posture scoring across all systems
  • Data sensitivity classification & shelf-life analysis
  • Quantum-vulnerable protocol inventory (SSH, VPN, PKI)
  • Executive risk report with remediation timeline
↗Typical outcome: Complete quantum exposure map with prioritized remediation plan in 2 weeks.
Crypto InventoryRisk ScoringNIST IR 8547

Quantum-Safe Architecture Design

Future-proof your infrastructure at the design level. We redesign your key management, certificate authority, and secure communications to be crypto-agile — ready to swap algorithms as NIST standards evolve without full rearchitecting.

  • Crypto-agile PKI and key management design
  • Zero-Trust network with PQC-hardened tunnels
  • HSM integration for quantum-resistant key storage
  • Algorithm agility framework for rapid future migration
↗Typical outcome: Architecture designed for 20+ year quantum resilience with zero rework on next standard update.
PKI DesignHSMCrypto-Agility

Quantum-Enhanced Machine Learning

Quantum computers deliver exponential speedups for specific ML workloads. We identify where quantum advantage applies in your AI pipeline — optimization, sampling, and feature-space expansion — and prototype hybrid quantum-classical models.

  • Quantum advantage opportunity assessment for your ML stack
  • Hybrid classical-quantum model prototyping (IBM Q, AWS Braket)
  • Quantum annealing for combinatorial optimization problems
  • Quantum-enhanced anomaly detection for security workloads
↗Typical outcome: 10–100× speedup on qualifying optimization workloads with hybrid quantum-classical pipelines.
IBM QuantumAWS BraketQiskit

Quantum Readiness Program

A structured 90-day program that takes your organization from quantum-unaware to quantum-ready — covering executive education, engineering upskilling, inventory completion, and your first PQC migration milestone shipped to production.

  • Executive quantum threat briefings & board reporting
  • Engineering team PQC upskilling workshops
  • Regulatory alignment (DORA, NIS2, NIST CSF 2.0)
  • First PQC algorithm deployed to production by day 90
↗Typical outcome: Board-approved quantum migration plan and first PQC deployment live within 90 days.
DORA ComplianceNIS2Exec Training

Quantum Threat Intelligence & Monitoring

The quantum threat landscape evolves fast. We continuously track nation-state quantum advances, NIST standard updates, and algorithm breaks — alerting your security team with actionable guidance before your window to act closes.

  • Continuous tracking of quantum capability advances
  • Automated alerts on NIST PQC standard changes
  • Nation-state quantum program intelligence briefings
  • Monthly quantum threat horizon reports
↗Typical outcome: 6–18 month advance warning on quantum threats before they impact your cryptographic assets.
Threat IntelNIST UpdatesNation-State
Cybersecurity

Defense-Grade Security
for Cloud-Native Infrastructure

From Zero-Trust architecture to 24/7 AI-assisted threat monitoring, we help enterprises build and maintain a resilient security posture — fully aligned with SOC 2, ISO 27001, HIPAA, and PCI-DSS frameworks.

Zero-Trust Architecture
24/7 SOC Coverage
AI-Assisted Threat Triage
Compliance-Aligned Controls
SIEM & SOAR Driven
24/7 AI-assisted SOC coverage across all managed clients
<9 min Mean time to detect across monitored environments
2.4M Security events processed per minute at peak
0 Breaches across managed clients in the last 14 months

SOC & Threat Monitoring

SIEM/SOAR-driven security operations with AI-assisted alert triage, threat hunting, and 24/7 monitoring across cloud workloads, endpoints, and network traffic.

  • AI-assisted alert triage & deduplication
  • 24/7 threat hunting across all layers
  • UEBA & behavioral baseline monitoring
  • Automated playbook-driven response
↗Typical outcome: MTTD reduced from 4+ hours to under 25 minutes.
SplunkMicrosoft SentinelElastic SIEM

Zero-Trust Architecture Design

Design and implement Zero-Trust network architectures — micro-segmentation, identity-aware proxy, and continuous verification to minimize blast radius from compromised credentials.

  • Network micro-segmentation design
  • Identity-aware proxy (BeyondCorp model)
  • Continuous device & user verification
  • Lateral movement prevention controls
↗Typical outcome: Attack blast radius reduced by 70%+ in hybrid cloud environments.
Cloudflare ZTZscalerBeyondCorp

Identity & Access Management

Privileged access management, just-in-time provisioning, MFA enforcement, and role-based access control for cloud and on-prem environments — with full audit logging.

  • Privileged access management (PAM)
  • Just-in-time access provisioning
  • MFA enforcement & SSO integration
  • RBAC & least-privilege audit trails
↗Typical outcome: 95% reduction in standing privileged access within 60 days.
OktaCyberArkAWS IAM

Cloud Security Posture Management

Continuous CSPM across AWS, Azure, and GCP — misconfiguration detection, automated remediation, and real-time compliance dashboards aligned to CIS Benchmarks and NIST CSF.

  • Multi-cloud misconfiguration detection
  • Automated remediation workflows
  • CIS Benchmark & NIST CSF alignment
  • Real-time compliance drift alerts
↗Typical outcome: Critical cloud misconfigs resolved within 2 hours of detection.
WizPrisma CloudAWS Security Hub

EDR/XDR Integration

Deploy and manage extended detection and response across endpoints, cloud workloads, and network — unified into a single pane with cross-layer threat correlation.

  • Unified endpoint & cloud XDR platform
  • Cross-layer threat correlation engine
  • Automated threat containment workflows
  • Forensic timeline & root-cause analysis
↗Typical outcome: Mean time to contain (MTTC) under 15 minutes for high-severity threats.
CrowdStrikeSentinelOneDefender XDR

Incident Response & Digital Forensics

Rapid incident containment, root-cause investigation, and post-incident reporting — with tabletop exercises and playbook development to reduce MTTR on future incidents.

  • Rapid triage & blast-radius containment
  • Digital forensics & evidence preservation
  • Post-incident root-cause reporting
  • Tabletop exercises & playbook authoring
↗Typical outcome: MTTR reduced by 50%+ with pre-built, environment-specific playbooks.
PICERL FrameworkVelociraptorTheHive
Compliance Framework Alignment
ISO 27001 SOC 2 Type II HIPAA PCI-DSS NIST CSF

Our security practice supports compliance initiatives aligned with each of these frameworks. Engagements include documentation and evidence artifacts that map directly to each standard's controls.

AI-First Security

Machine Learning at the Core
of Every Security Decision

Security tools generate enormous volumes of alerts. Our AI-first approach cuts through the noise — prioritizing real threats, accelerating response, and continuously learning from your environment.

2.4M Events analyzed / min
70% Alert fatigue reduction
<22m Mean time to detect
  • Automated Threat Detection Real-time

    ML models trained on threat intelligence feeds and your environment's baseline behavior detect novel attacks that signature-based tools miss.

  • Intelligent Alert Triage 70% reduction

    AI correlates events across your SIEM, cloud logs, and endpoint telemetry to surface only high-confidence, actionable alerts — eliminating analyst fatigue.

  • Behavioral Analysis (UEBA) Continuous

    Continuous profiling of users and service accounts — flagging lateral movement, privilege escalation, and data exfiltration patterns in real time.

  • Deployment Risk Scoring Pre-release

    Every deployment is scored for security risk before it reaches production — analyzing code changes, dependency delta, and infrastructure impact with a trained model.

DevOps

Accelerate Delivery Velocity. Eliminate Deployment Risk.

We architect and automate the pipelines, infrastructure, and reliability practices that enable engineering teams to deploy with confidence — reducing lead time from commit to production from weeks to minutes, without compromising stability or compliance.

15× More frequent deployments after pipeline rebuild
<1 hr Lead time from commit to production — elite DORA tier
42 min Median time to restore service across managed environments
<5% Change failure rate after reliability engineering pass

CI/CD Pipeline Design & Automation

Fully automated pipelines on GitHub Actions, GitLab CI, or Azure DevOps — with parallel testing, artifact management, and gated deployments to staging and production environments.

Infrastructure as Code

Repeatable, auditable cloud infrastructure with Terraform and CloudFormation. Drift detection, state management, and module libraries for AWS, Azure, and GCP environments.

Containerization & Kubernetes

Docker-based application packaging, Kubernetes cluster design, Helm chart authoring, and GitOps workflows with ArgoCD or Flux for declarative, version-controlled delivery.

SRE & Cloud Cost Optimization

SLO/SLA definition, error budget management, chaos engineering, and FinOps practices to reduce cloud spend by 20–40% without sacrificing reliability or performance.

DevSecOps

Security Woven Into Every Stage of Delivery

We embed security as a first-class concern at every point of your software delivery lifecycle — so compliance is continuous, not a last-minute gate.

92% Of vulnerabilities caught before they reach production
<8 min Full security scan suite added to CI — no pipeline slowdown
100% Compliance evidence auto-collected for SOC 2 / ISO 27001
65% Fewer false-positive findings after policy & alert tuning
01
Plan
  • Threat modeling
  • Security stories
  • Risk assessment
02
Code
  • Pre-commit hooks
  • IDE security linting
  • Secure code templates
03
Build
  • SAST & SCA
  • SBOM generation
  • Dependency audit
04
Test
  • DAST scanning
  • Container scanning
  • Pen-test automation
05
Release
  • Policy-as-code gates
  • Secrets validation
  • Compliance sign-off
06
Operate
  • Runtime monitoring
  • Compliance reporting
  • Incident response

Secure CI/CD Pipelines

Integrate SAST, DAST, SCA, and dependency scanning directly into your pipelines — with configurable break-the-build severity thresholds that stop vulnerabilities before they ship.

  • SAST & DAST gates on every PR
  • Software Composition Analysis (SCA)
  • Configurable severity thresholds
  • Pipeline-native SBOM generation
↗ Typical outcome: Critical vulnerabilities blocked at build — not discovered in production.
Snyk Semgrep Trivy OWASP ZAP

Policy-as-Code & Compliance Automation

Define security and compliance rules as versioned code using OPA/Rego or Sentinel — enforced automatically on every deployment with full audit trails for SOC 2 and ISO 27001.

  • OPA/Rego policy authoring & testing
  • Automated SOC 2 evidence collection
  • Drift detection & remediation alerts
  • Policy version control & rollback
↗ Typical outcome: Audit prep time reduced from 6 weeks to under 5 days.
OPA / Rego Sentinel Conftest

Secrets Management Integration

Eliminate hardcoded credentials with HashiCorp Vault, AWS KMS, and Azure Key Vault — including dynamic secret injection, automated rotation, and repo-wide secrets scanning.

  • Dynamic secrets with zero-TTL leases
  • Automated rotation pipelines
  • Git repo secrets scanning & remediation
  • Least-privilege access enforcement
↗ Typical outcome: Zero hardcoded credentials in 90 days across all repositories.
HashiCorp Vault AWS KMS TruffleHog

Developer Security Guardrails

Pre-commit hooks, secure code templates, and IDE-integrated linting that surface vulnerabilities at the developer's keyboard — slashing the cost of remediation by an order of magnitude.

  • IDE plugins for real-time vuln feedback
  • Pre-commit hooks with auto-remediation
  • Secure coding standards & templates
  • Developer security training integration
↗ Typical outcome: 80% of security findings caught before code review — not in production.
SonarQube pre-commit Bandit
Industry Solutions

Built for the Industries
That Can't Afford to Fail

Real-world engagements for organizations where security, compliance, and delivery velocity are simultaneously non-negotiable.

🏭 FinTech & Payments ☁ Cloud-Native SaaS 🏥 Healthcare & Life Sciences 🏢 Enterprise & Manufacturing
FinTech
6 wks → 4 days Audit prep time

Continuous PCI-DSS Compliance for a Payments Platform

Challenge

A payments platform deploying dozens of times daily had a 3-week lag between changes and compliance evidence — manual audits couldn't keep pace with their release velocity.

Solution

Policy-as-code gates in CI/CD, automated PCI-DSS evidence collection, and an AI model that flags compliance-impacting changes before merge — zero disruption to engineer workflow.

  • ✔ Compliance evidence auto-generated on every deploy
  • ✔ Audit preparation: 6 weeks → 4 days
  • ✔ Zero compliance gaps across 14 months of CD
Policy-as-Code PCI-DSS AI/ML
Cloud-Native
4.5 hrs → 22 min Mean time to detect

AI Threat Detection for an 800-Microservice Kubernetes Platform

Challenge

A SaaS platform's SIEM generated 50,000 alerts/day — analysts were triaging less than 15%, leaving real threats buried in noise across 800+ microservices.

Solution

ML-based alert correlation engine trained on historical data, enriched with commercial CTI feeds, integrated with existing SIEM via API — deployed in 3 weeks, no rip-and-replace.

  • ✔ Alert volume reduced 68% through intelligent deduplication
  • ✔ MTTD improved: 4.5 hours → 22 minutes
  • ✔ Analysts handle 3× more true positives with same headcount
ML / SIEM Kubernetes CTI Feeds
Healthcare
3 wks → 2 days Model deployment time

HIPAA-Aligned MLOps Platform for Healthcare Analytics

Challenge

A healthcare analytics provider needed to train ML models on PHI data with full HIPAA compliance, per-version audit trails, and zero risk of data exposure across the pipeline.

Solution

HIPAA-aligned MLOps platform with encrypted feature stores, differential privacy controls, model lineage tracking, and automated access auditing on Zero-Trust Kubernetes.

  • ✔ All HIPAA technical safeguards addressed in the ML pipeline
  • ✔ Deployment cycle: 3 weeks → 2 days
  • ✔ Zero PHI exposure events since platform launch
MLOps HIPAA Zero-Trust
Enterprise
Quarterly → Weekly Deployment frequency

DevSecOps Modernization for a Fortune 500 Manufacturer

Challenge

A Fortune 500 manufacturer shipping monolithic apps quarterly with no automated security testing — critical vulnerabilities were routinely reaching production environments.

Solution

Phased modernization: containerization, Git-based CI/CD with SAST/DAST gates, developer secure coding training, and continuous CSPM across their AWS migration workloads.

  • ✔ Deployment frequency: quarterly → weekly sprints
  • ✔ High-severity production vulnerabilities down 60%
  • ✔ Developer security training: 94% completion rate
DevSecOps SAST/DAST CSPM
Verified Client Results

Proven Outcomes at Scale

Real numbers from production engagements — not benchmarks, not best-case projections. Verified with client sign-off.

60+ Engagements delivered
98% Client retention rate
$0 Production breaches across all clients
4.9/5 Average client satisfaction score
🏚 Insurance 4-month engagement
45%
Faster incident response
Before 2.7 hrs avg MTTR
After <90 min avg MTTR

Global Insurance Carrier

Integrated AI-assisted triage into a legacy SOC workflow. Analysts now resolve critical incidents in under 90 minutes on average — SOAR playbooks fully automated 55% of tier-1 responses.

  • ✔ SOAR automated 55% of tier-1 responses entirely
  • ✔ Alert triage time reduced by 70%
  • ✔ Zero P1 SLA breaches in 12 months post-deployment
SOC Modernization AI/ML SOAR
☁ Cloud SaaS 3-month engagement
3.2x
Deployment velocity increase
Before 18 days lead time
After <6 hours lead time

Series C SaaS Platform

Rebuilt CI/CD pipelines on GitHub Actions with Terraform-managed infrastructure and Kubernetes GitOps. Lead time dropped 3.2× with a 40% reduction in post-deploy incidents.

  • ✔ Lead time: 18 days → under 6 hours
  • ✔ Post-deploy incidents reduced 40%
  • ✔ Infrastructure drift eliminated with GitOps
DevOps Kubernetes IaC
🏭 Banking 6-month engagement
60%
Fewer critical vulnerabilities
Before No automated security
After Full DevSecOps pipeline

Regional Banking Group

Implemented DevSecOps across 12 development teams: SAST/DAST in pipelines, secrets scanning, and a central policy-as-code engine. Critical vulnerabilities fell 60% within 6 months.

  • ✔ Critical vulns in production down 60% in 6 months
  • ✔ 12 teams onboarded with zero pipeline downtime
  • ✔ SOC 2 audit evidence generated automatically
DevSecOps SAST/DAST Policy-as-Code
How It Works

One Platform, Four Disciplines, Continuous Loop

anyops.ai operates as a continuous intelligence loop — ingesting signals, analyzing them with ML, automating responses, and feeding learnings back into the system.

Ingest

Collect code commits, pipeline events, cloud logs, network flows, and endpoint telemetry from across your environment — normalized into a unified data schema.

  • GitHub, GitLab, Azure DevOps
  • CloudTrail, VPC Flow Logs, K8s audit logs
  • SIEM connectors (Splunk, Sentinel, Elastic)

Analyze

ML models and rule engines process incoming data streams in real time — detecting anomalies, scoring risk, correlating events, and generating threat hypotheses.

  • Anomaly detection models
  • Behavioral baselines (UEBA)
  • Deployment risk scoring

Automate

Policy-as-code engines and SOAR playbooks act on analysis results — blocking risky deployments, isolating compromised workloads, or triggering remediation workflows.

  • OPA/Rego policy enforcement
  • SOAR playbook execution
  • Auto-remediation via IaC

Respond

Human-in-the-loop escalation for high-confidence threats — with AI-generated summaries, enriched context, and recommended actions that cut analyst decision time by 60%.

  • AI-enriched incident briefings
  • Slack / PagerDuty / Jira integration
  • Post-incident model retraining
↻ Outcomes feed back into model retraining — the system improves with every incident.
Engagement Models

Start Where You Are. Scale as You Grow.

Every organization is at a different point in its security and DevOps maturity. We offer flexible engagement models that meet you there.

Discovery
4–6 weeks

Discovery & Strategy Engagement

Best for: Teams evaluating their security posture

A time-boxed assessment of your current security posture, DevOps maturity, and AI readiness. Deliverables include a prioritized roadmap, gap analysis, and executive presentation.

  • ✔ Security posture assessment
  • ✔ DevOps pipeline audit
  • ✔ AI/ML opportunity analysis
  • ✔ Prioritized roadmap
Discuss this engagement
Most Popular
Implementation
3–6 months

Implementation Projects

Best for: Teams ready to build & ship production infrastructure

Hands-on delivery of a defined scope: a DevSecOps pipeline build-out, Zero-Trust architecture implementation, or ML platform deployment — with knowledge transfer to your team.

  • ✔ Defined scope & deliverables
  • ✔ Embedded delivery team
  • ✔ Documentation & runbooks
  • ✔ Team training & enablement
Start a project
Managed
12+ months ongoing

Managed DevSecOps & Security Operations

Best for: Orgs that need a continuous security operations capability

Ongoing managed service for security operations, compliance monitoring, and DevOps platform management — with SLA-backed response times and monthly reporting.

  • ✔ 24/7 security monitoring
  • ✔ Continuous compliance reporting
  • ✔ Pipeline health management
  • ✔ Monthly executive reviews
Learn more
AI Co-Pilot
Per-seat · Monthly

AI Co-Pilot for DevOps Teams

Best for: Engineering teams that want AI insights without a full engagement

A managed ML-driven recommendation layer that sits alongside your existing tools — surfacing deployment risks, infrastructure anomalies, and cost optimization opportunities in your daily workflow.

  • ✔ Deployment risk scoring
  • ✔ Anomaly alerts in Slack/Teams
  • ✔ Cost optimization recommendations
  • ✔ Integrates with existing toolchain
Request a demo
Resources

Knowledge for Security-Minded Engineers

Practical guides, playbooks, and research — no marketing fluff, just content written by practitioners.

Guide

The DevSecOps Playbook

A 60-page practitioner guide to embedding security into every stage of your CI/CD pipeline — with tool recommendations, policy templates, and a maturity model.

60 pages · 45 min read
Download Free →
Whitepaper

AI for Cloud Security: Separating Signal from Noise

Research paper on applying ML-based anomaly detection to cloud environments — covering data requirements, model selection, and common failure modes to avoid.

28 pages · 20 min read
Download Free →
Webinar

Zero Trust in a Hybrid Cloud World

On-demand webinar: how to design and incrementally implement a Zero-Trust architecture across AWS, Azure, and on-prem — without a "rip and replace" approach.

55 min · On-demand
Watch Free →
Guide

MLOps for Regulated Industries

Step-by-step guide to building HIPAA- and PCI-DSS-aligned ML pipelines — covering data governance, model auditing, and compliant feature store architectures.

48 pages · 35 min read
Download Free →
Webinar

Kubernetes Security Hardening in Practice

Live walkthroughs of RBAC configuration, network policy design, admission controllers, and runtime security with Falco — for teams running production Kubernetes.

Coming Soon
Whitepaper

SBOM as a Security Control

How software bill of materials generation, continuous analysis, and vulnerability correlation can become an operational security control — not just a compliance artifact.

Coming Soon
Client Testimonials

Trusted by Security-First Teams

From Fortune 500 enterprises to Series C hypergrowth platforms — hear directly from the engineering leaders who've deployed with us.

★★★★★

“anyops.ai didn’t just deliver a report — they shipped production infrastructure on day one of the engagement and handed it to our team fully documented. Their FDE model is genuinely different from traditional consulting. Three months in, our deployment velocity is 3× what it was, and we haven’t had a single P1 security incident since.”

R
Rajesh M. CISO · Global Insurance Carrier
6-month FDE
★★★★★

“We went from deploying quarterly to shipping multiple times a day. The anyops.ai team built the entire CI/CD and security pipeline inside our environment — fully documented and owned by our engineers on day one.”

S
Sarah K. VP Engineering · Series C SaaS Platform
3-month build
★★★★★

“Building a HIPAA-aligned MLOps platform on PHI data is genuinely hard. anyops.ai solved it in 10 weeks and handed us a complete, auditable system. Our model deployment cycle went from 3 weeks to under 48 hours.”

A
Arjun P. Head of Platform Eng · Healthcare Analytics
MLOps platform
Get in Touch

Let's Solve Your Security
& Delivery Challenges

We work with organizations serious about security, compliance, and engineering excellence. Describe your situation — we'll give you an honest assessment of fit and respond within one business day.

  • No sales pressure — honest assessment of fit
  • First conversation is always free
  • Speak directly with engineers, not account managers
Email us directly info@anyops.ai
Response time Within 1 business day
First call Always free — no obligation
Who you'll talk to Senior engineers, not sales reps
What Happens After You Submit
  1. 01
    Research & Context Review
    We review your message, research your company, tech stack, and current challenges before reaching out.
    🕑 Same business day
  2. 02
    Engineer Reaches Out
    A senior engineer — not a sales rep — contacts you directly with context-aware questions tailored to your situation.
    🕑 Within 1 business day
  3. 03
    30-Min Discovery Call
    An honest, no-obligation conversation. We assess fit together — if we're not the right match, we'll say so and recommend who is.
    🕑 30 minutes  ·  No obligation
  4. 04
    Engagement Proposal
    If there's a strong fit, we send a clear scope, timeline, and approach document within 48 hours. No surprises.
    🕑 Within 48 hours of call
★★★★★
"anyops.ai didn't just deliver a report — they shipped production infrastructure on day one of the engagement and handed it to our team fully documented."
Krishna Senior Vice President  ·  Series B FinTech 4-month FDE engagement