Part 4: Know Your Enemy – Understanding the Modern Threat Landscape for Data & Applications

After analyzing business drivers, strategic alignment, and regulatory requirements, it’s essential to understand a key fact: cyber threats are not just hypothetical; they are real, constantly evolving, and critical to business. No matter how well your systems are built, there is always a risk of exposure.

Understanding the threat landscape is important, but it should not lead to fearmongering. Instead, it aims to give decision-makers, developers, and security teams the awareness they need to protect what matters most. This post will examine how attackers target modern applications and data, identify their patterns, and show how organizations can respond wisely, not just reactively.

Before we dive in, if you’re looking for real data on cybersecurity trends, key vendors, and market direction, explore our Cybersecurity Intelligence Page. You’ll find free reports and insights to help you make confident decisions.

Why the Threat Landscape Has Changed

Historically, cybersecurity focused on the network perimeter, such as firewalls, intrusion detection systems, and anti-virus software. In today’s digital world, data is everywhere, and applications are no longer limited to traditional IT boundaries. Several trends have greatly increased the attack surface:

• Cloud adoption: APIs, containers, and multi-cloud architectures create new misconfiguration risks.
• Remote work & BYOD: Access happens from anywhere, often on untrusted devices.
• Complex supply chains: Software often includes open-source dependencies and third-party services.
• Fast release cycles: CI/CD pipelines push changes daily, sometimes without a full security review.
• Rising automation by attackers: Bots, toolkits, and AI-driven attacks make exploitation faster and more precise.

Attackers use the same strategy as businesses: find where data is stored and where controls are weakest. As a result, the focus is shifting to application and data layers.

The Most Common Threats to Application and Data Security

The following is a list of the most common and significant threats that organizations encounter today, categorized by how they typically happen:

1. Application-Level Attacks

These target flaws in how apps are built, exposed, or interact with users:

• SQL Injection / Command Injection: Malicious input is used to manipulate backend queries or gain database access.
• Cross-Site Scripting (XSS): Injecting scripts into web pages to steal session tokens or manipulate user data.
• Broken Authentication / Authorization: Weak or misconfigured login mechanisms (e.g., exposed tokens, bypassable logic).
• Insecure APIs: Poorly protected or undocumented endpoints that leak or expose data.
• Excessive Permissions: Apps or users with more access than needed, making lateral movement easier.

2. API and Microservices Abuse

In modern architectures, APIs are the glue and the risk:

• Mass enumeration & scraping: Automated attacks are designed to extract data from public or undocumented APIs.
• Broken object-level authorization: Attackers modify IDs in requests to access unauthorized data (e.g., user A viewing user B’s account).
• Injection into serialized objects or JSON bodies: Exploiting deserialization vulnerabilities in loosely validated input.

3. CI/CD and Development Pipeline Risks

The speed of modern software delivery can hide subtle risks:

• Supply chain attacks: Malicious or compromised open-source libraries are injected via package managers.
• Code injection in build systems: CI/CD misconfigurations that allow unauthorized script execution during build or deployment.
• Secrets in source code: Hardcoded passwords, tokens, or keys accidentally pushed to public repos.

4. Cloud Misconfigurations

Cloud flexibility is a strength, but also a source of breaches:

• Publicly exposed buckets or storage blobs: Files with sensitive data were inadvertently left open.
• Over-permissive IAM roles: Identities with excessive rights, increasing blast radius in case of compromise.
• Lack of logging and monitoring: Inability to detect malicious activity or prove what happened after the fact. 

5. Credential-Based and Social Engineering Attacks

Not all attacks are technical; some are psychological:

• Credential stuffing: Reusing passwords from previous breaches to access accounts.
• Phishing and session hijacking: Tricking users into giving up credentials or clicking on malicious links.
• Business email compromise (BEC): Impersonation attacks targeting executives or finance teams. 

6. Insider and Shadow IT Threats

Sometimes the danger comes from within:

Unintentional insider errors: Mistakes like sending data to the wrong recipient or deleting records.
Intentional insider misuse: Employees abusing access to exfiltrate or manipulate data.
Shadow IT: Teams deploying apps or data stores outside security oversight (e.g., unauthorized SaaS tools). 

Threat Intelligence in Practice

It’s not enough to list threats; you need to monitor for them. Mature organizations use:

• Threat modeling during development to identify potential attack paths.
• Threat intelligence feeds to stay updated on TTPs (tactics, techniques, and procedures).
• Security testing tools like SAST, DAST, and API fuzzers.
• Red teaming and bug bounty programs to simulate or incentivize real-world attacks.

Understanding the threat landscape isn’t about fixing everything; it’s about prioritizing risk, building visibility, and preparing for incidents.

How Attackers Exploit Gaps Between App and Data Security

One of the most dangerous patterns we see is this:

1. A developer builds an API for internal use, but doesn’t enforce proper authentication.
2. A legacy database contains sensitive personal data without encryption at rest.
3. A marketing team uploads this data to a cloud dashboard using shared credentials.
4. An attacker finds the exposed endpoint, accesses the dashboard, and exfiltrates data, without tripping a single alarm.

These cascading failures are not caused by a single tool malfunction but result from a lack of integration between the application’s controls, data, and users.

This highlights the importance of defense in depth. Relying solely on input validation isn’t enough to ensure security if other vulnerabilities exist.

Next Up: Designing for Security – Architecture & Design Principles

In the next post of our series, we will shift from discussing risk awareness to focusing on proactive design. In Part 5, we will examine how to design modern applications and data systems securely. We will start by looking at architecture, access models, and design principles such as Zero Trust, segregation of duties, and secure defaults. It is crucial to implement security measures proactively, even before writing any code.