Build defenses that hold.

What Is Security Engineering

Security engineering is the discipline of designing and building systems that remain dependable in the face of malice, error, and misfortune. Where security operations focuses on monitoring, detecting, and responding to threats in real time, security engineering focuses on the architecture, configuration, and tooling that make effective operations possible.

Think of it this way: security operations is the team watching the cameras and responding to alarms. Security engineering is the team that designed the building, installed the locks, positioned the cameras, and wrote the alarm rules. Both are essential, but without solid engineering foundations, operations teams are fighting with broken tools in a building full of unlocked doors.

The philosophical foundation of security engineering is defense in depth — the principle that no single security control should be the sole barrier between an attacker and a critical asset. Effective defense requires multiple layers: network controls, identity and access management, endpoint protection, application security, data protection, and monitoring. Each layer should be designed so that if it fails, the layers behind it still provide protection, and the failure itself is detected.

Defense in depth is not the same as defense in redundancy. Simply deploying five different firewalls in sequence doesn't create depth if they all fail to the same attack technique. True depth means layering different types of controls that address different attack vectors, so that the skills and resources required to breach each successive layer increase, and the attacker's opportunity for detection multiplies at each stage.

Security engineering is also fundamentally about making the right trade-offs. Perfect security is unachievable and would be unusable if it existed. Every engineering decision involves balancing security against usability, performance, cost, and operational complexity. The goal is not to eliminate all risk — it's to reduce risk to a level that is acceptable given the organization's threat model, regulatory obligations, and business requirements.

Architecture Review

An architecture review examines the design of an organization's IT environment from a security perspective. It's the foundational security engineering engagement — understanding how systems are connected, how data flows, where trust boundaries exist, and where the design itself creates or mitigates risk.

What We Examine

Network Topology

How the network is segmented, where trust boundaries lie, which systems can communicate with which, and whether the segmentation actually enforces the intended security policy. We review firewall rule sets, routing configurations, VLAN assignments, and network access control (NAC) implementations. Common findings include: flat networks where a compromised workstation has direct access to database servers, overly permissive firewall rules accumulated over years of change requests, and VPN configurations that place remote users on the same network segment as critical infrastructure.

Identity and Access Management

How users are authenticated, how access is authorized, how privileges are managed, and how the identity infrastructure is secured. We review Active Directory architecture (forest and domain structure, trust relationships, group policy configuration), privileged access management, multi-factor authentication deployment, service account inventory, and password policies. Identity infrastructure is the most consistently targeted component in enterprise attacks — Kerberoasting, Golden Ticket attacks, and credential relay techniques all exploit weaknesses in identity architecture.

Data Flow Analysis

Where sensitive data resides, how it moves between systems, who has access, and what protections exist at each stage. We map data flows for the organization's most critical information assets — customer data, financial records, intellectual property, credentials — and evaluate whether encryption, access controls, and monitoring are appropriate at each point in the flow. This analysis frequently reveals unexpected data exposures: copies of production databases in development environments, sensitive data traversing unencrypted internal network links, or backup systems that store data with weaker protections than the primary systems.

Cloud Architecture

For organizations using AWS, Azure, or GCP, we review the cloud account structure, IAM policies, network architecture (VPCs, security groups, network ACLs), data storage configuration, logging and monitoring setup, and the boundary between cloud and on-premises environments. Cloud architectures often grow organically, and the security posture of year-one resources rarely matches the organization's current security standards.

Trust Boundaries

Trust boundaries define where one security domain ends and another begins. Effective architecture establishes clear trust boundaries and enforces them with appropriate controls. We examine where trust boundaries exist (or should exist), whether controls at each boundary are adequate, and whether the organization's implicit trust assumptions match reality. A common finding: the organization treats its internal network as trusted, but any attacker who achieves initial access through phishing, VPN compromise, or a supply chain attack is immediately inside that trusted zone with unrestricted lateral movement.

Common Findings and Recommendations

Architecture reviews consistently surface several categories of findings. Excessive implicit trust — where systems communicate without authentication or authorization simply because they share a network segment. Over-privileged accounts — where service accounts and administrative accounts have broader permissions than their function requires. Missing monitoring — where critical systems or network segments generate no security telemetry and are effectively invisible to the SOC. And architectural debt — where legacy design decisions that made sense years ago now create security gaps in the current threat environment.

Infrastructure Hardening

Infrastructure hardening is the process of reducing the attack surface of systems, networks, and services by removing unnecessary functionality, applying secure configurations, and implementing additional protective controls. It is methodical, often unglamorous work — but it eliminates the low-hanging fruit that attackers rely on for initial access and lateral movement.

Operating System Hardening

We harden operating systems against CIS (Center for Internet Security) Benchmarks — industry-standard configuration baselines for Windows, Linux, and macOS. This includes: disabling unnecessary services and protocols, configuring host-based firewalls, restricting local administrator access, enabling audit logging, hardening authentication mechanisms, removing default accounts and credentials, and applying file system permissions that follow least privilege. CIS Benchmarks provide two profile levels: Level 1 (practical hardening with minimal operational impact) and Level 2 (deeper hardening for high-security environments). We help organizations determine the right profile for each system class and build automated compliance checking into their deployment pipeline.

Network Segmentation

Effective segmentation limits the blast radius of a compromise by preventing an attacker from moving freely between network zones. We design segmentation strategies based on data sensitivity, system function, and threat model — separating user workstations from servers, production from development, management networks from data networks, and internet-facing systems from internal infrastructure. Segmentation is implemented through a combination of VLANs, firewall rules, and increasingly, microsegmentation technologies that enforce policy at the workload level rather than the network perimeter.

Email Security

Email remains the most common initial access vector. We implement and configure email security controls including: SPF, DKIM, and DMARC for domain authentication (preventing spoofing of the organization's domain), secure email gateway configuration with attachment sandboxing and URL rewriting, mailbox audit logging, external email tagging, and policies for handling sensitive data via email. We also review Exchange or Microsoft 365 configurations for common misconfigurations that expose the organization — such as overly permissive mail flow rules, shared mailboxes with excessive access, or legacy authentication protocols that bypass MFA.

DNS Security

DNS is both a critical dependency and a frequently exploited protocol. We implement DNSSEC where appropriate, configure DNS logging for security monitoring, deploy DNS filtering to block known malicious domains, and review internal DNS architecture for zones that leak internal naming information. DNS-based attacks — including DNS tunneling for data exfiltration, DNS rebinding, and cache poisoning — are often overlooked in hardening programs, but they remain effective in environments that treat DNS as purely an infrastructure service with no security relevance.

Endpoint Protection

We review and optimize endpoint protection platforms (EPP) and endpoint detection and response (EDR) deployments. This includes: validating coverage (ensuring every endpoint is enrolled and reporting), tuning policies to balance protection with operational impact, configuring advanced features like memory protection, credential theft prevention, and behavioral detection, and ensuring that EDR telemetry feeds into the SIEM for correlation with other data sources. Many organizations deploy EDR but use only a fraction of its capabilities — often running it in "detect only" mode indefinitely, or never tuning the behavioral detection rules for their environment.

Detection Engineering

Detection engineering is the practice of designing, building, testing, and maintaining the detection rules and analytics that identify malicious activity within an environment. It's the bridge between security monitoring tools (SIEM, EDR, NDR) and the analysts who respond to alerts. Done well, it produces high-fidelity alerts that surface real threats. Done poorly, it produces alert fatigue, missed attacks, and a SOC team drowning in false positives.

Building Detection Rules

Effective detection starts with a clear understanding of what you're trying to detect. We use threat-informed detection development: starting from known attacker techniques (mapped to MITRE ATT&CK), identifying the telemetry sources that would reveal each technique, and building detection logic that reliably identifies the technique while minimizing false positives in the client's specific environment.

For example, detecting Kerberoasting (ATT&CK T1558.003) requires: Windows Security Event logs with Kerberos service ticket request events (Event ID 4769), filtering for encryption types that indicate offline crackable tickets (RC4, specifically encryption type 0x17), and baseline analysis to distinguish legitimate service ticket requests from attack patterns. The detection logic must account for normal environment behavior — a web server that legitimately requests hundreds of service tickets per day should not generate the same alert as a user workstation that suddenly requests tickets for 50 different service accounts.

SIEM Tuning

Most SIEM deployments suffer from one of two problems: too many alerts of low quality (alert fatigue), or too few alerts with critical gaps in coverage. Tuning addresses both. We review existing detection rules, eliminate or refine those with high false positive rates, identify coverage gaps by mapping detections against ATT&CK, and establish ongoing tuning processes that treat detection rules as code that requires maintenance.

Alert Quality Metrics

You can't improve what you don't measure. We help organizations establish metrics for their detection capability:

• Precision: The percentage of alerts that represent true positive security events. Low precision means analyst time is wasted investigating false positives.
• Recall: The percentage of actual security events that generate an alert. Low recall means threats are being missed.
• Mean Time to Detect (MTTD): How long between an attacker's action and the generation of an alert. Lower is better.
• Mean Time to Respond (MTTR): How long between alert generation and the completion of initial response actions. This measures the combined efficiency of detection and operations.

Detection-as-Code

Modern detection engineering treats detection rules the same way software engineering treats application code: version-controlled, peer-reviewed, tested, and deployed through a CI/CD pipeline. Detection-as-code provides: change tracking (who modified this rule and why), rollback capability, automated testing against known-good and known-bad data, and consistent deployment across environments. We help organizations implement detection-as-code pipelines using tools like SIGMA rules (a vendor-agnostic detection rule format) that can be compiled to the client's specific SIEM platform.

SIGMA Rules

SIGMA is an open standard for writing detection rules in a SIEM-agnostic YAML format. A single SIGMA rule can be compiled to Splunk SPL, Elastic Query Language, Microsoft Sentinel KQL, or dozens of other platforms. This eliminates vendor lock-in for detection content, enables sharing of detection logic across the community, and allows organizations to maintain a single canonical set of detection rules regardless of their SIEM platform. ForgeWork develops custom SIGMA rules for client environments and contributes to the open-source SIGMA rule repository.

Zero Trust Architecture

Zero Trust is a security model based on the principle that no user, device, or network segment should be inherently trusted. Instead of the traditional castle-and-moat approach (where everything inside the perimeter is trusted), Zero Trust requires continuous verification of every access request based on all available context.

The concept originated with John Kindervag at Forrester Research in 2010 and has been formalized by NIST in Special Publication 800-207 (Zero Trust Architecture). It rests on three core principles:

Verify Explicitly

Every access request is authenticated and authorized based on all available data points: user identity, device health, location, resource sensitivity, and behavioral anomalies. This contrasts with traditional models where authentication happens once (at the VPN gateway or domain login) and subsequent access within the trusted zone is largely unchecked. In a Zero Trust model, a user who authenticated five minutes ago must still pass verification when accessing a new resource, and the verification may consider additional context — is this a managed device? Is the user's behavior consistent with their normal patterns? Is the resource they're requesting consistent with their role?

Least Privilege

Access is granted at the minimum level required for the task at hand, using just-in-time (JIT) and just-enough-access (JEA) policies. Privileged access is time-limited and purpose-specific rather than persistent. This limits the blast radius of compromised credentials — if a user's account is compromised, the attacker only gains access to the resources that user can reach, and administrative access requires additional verification steps that the attacker may not be able to satisfy.

Assume Breach

Design systems and controls under the assumption that an attacker is already present in the environment. This drives investment in monitoring, anomaly detection, micro-segmentation, and automated response — because if you assume the perimeter has already been breached, you focus on limiting what the attacker can do once inside, rather than solely on keeping them out.

Practical Implementation

Zero Trust is a strategy, not a product. Implementation is a multi-year journey that typically starts with identity (strong authentication, conditional access policies, privileged access management) and progressively extends to device health verification, network micro-segmentation, application-level access controls, and data classification-driven policies. ForgeWork helps organizations develop a pragmatic Zero Trust roadmap that delivers incremental security improvements at each stage, rather than waiting for a complete transformation that may never arrive.

Cloud Security Posture

Cloud environments introduce a fundamentally different security model. The shared responsibility model means that the cloud provider secures the infrastructure, but the customer is responsible for securing their configurations, data, access policies, and workloads. Misunderstanding this boundary is the root cause of most cloud security incidents.

Cloud Security Posture Management (CSPM)

CSPM tools continuously monitor cloud configurations against security baselines and compliance frameworks. We help organizations deploy and tune CSPM solutions, establish remediation workflows for findings, and build the organizational processes to keep pace with the rate of configuration change in cloud environments. Without CSPM, cloud misconfigurations can persist for months before discovery — if they're discovered at all before an attacker finds them.

IAM Review

Cloud IAM is the most critical and most complex component of cloud security. We review IAM policies for overly permissive access, analyze cross-account access patterns, identify dormant and orphaned roles, evaluate service-linked roles and their permissions, and assess the use of temporary credentials versus long-lived access keys. A single overly permissive IAM policy can negate every other security control in the cloud environment.

Encryption and Data Protection

We review encryption implementations for data at rest (storage, databases, backups) and in transit (TLS configurations, certificate management, internal traffic encryption). We evaluate key management practices — who has access to encryption keys, how are they rotated, are they stored securely — and identify data stores that lack appropriate encryption or access controls.

Logging and Monitoring

Cloud environments generate extensive audit trails — CloudTrail in AWS, Activity Log in Azure, Cloud Audit Logs in GCP — but many organizations fail to enable all relevant log sources, store logs for an adequate retention period, or actually monitor them for security events. We review logging configuration, ensure that critical events are captured and forwarded to the SIEM, and build detection rules specific to cloud attack techniques.

Container and Serverless Security

Container environments (Docker, Kubernetes) and serverless platforms (Lambda, Azure Functions, Cloud Functions) introduce unique security considerations: image vulnerability management, runtime protection, secrets management, network policy enforcement, and the expanded attack surface created by container orchestration APIs. We review container security configurations, assess Kubernetes RBAC policies, evaluate image scanning and admission control pipelines, and test serverless function configurations for common vulnerabilities.

Our Delivery Model

Security engineering engagements follow a structured delivery model designed to produce lasting improvements — not just a report that describes what should be done, but actual implementation and knowledge transfer that leaves the client's team more capable.

Assessment Phase

We begin by understanding the current state: reviewing existing architecture, configurations, and documentation; interviewing key technical staff; and mapping the environment's security posture against relevant frameworks and benchmarks. This phase produces a detailed assessment report with prioritized findings and recommendations.

Design Phase

Based on assessment findings and the client's priorities, we design solutions that address the identified gaps. Design work includes: architectural diagrams, configuration specifications, implementation plans, and — critically — a clear definition of success criteria that will be used to validate the implementation. Design documents are reviewed with the client's technical team before implementation begins, ensuring alignment and surfacing any operational constraints early.

Implementation

We implement the designed solutions in the client's environment, working alongside their technical team. For high-risk changes (network segmentation modifications, identity infrastructure changes, detection rule deployments), we follow a staged approach: implement in monitoring mode first, validate behavior, then enforce. This approach minimizes operational disruption while ensuring security improvements are effective.

Knowledge Transfer

Every engineering engagement includes dedicated knowledge transfer sessions where we walk the client's team through what was implemented, why it was implemented that way, how to maintain it, and how to extend it as the environment evolves. We provide runbooks for ongoing operations and troubleshooting guides for common issues. The goal is for the client's team to own and operate the new capabilities independently after the engagement ends.

Documentation

All work is documented: architecture decisions, configuration changes, detection rule logic, operational procedures, and maintenance requirements. Documentation is delivered in the client's preferred format and integrated into their existing documentation systems. Good documentation is the difference between a security improvement that persists and one that degrades the moment the consulting team leaves.

Related Resources

• Threat Assessment & Penetration Testing — Identify gaps before you engineer solutions.
• ForgeWork Insights — Technical articles on security engineering topics.