How to Build an AI Security Framework for DevSecOps Teams
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Software developers have a new, unexpected headache.
As AI becomes more deeply embedded in software applications, it creates new attack surfaces that traditional AppSec programs do not fully address on their own. Developers need a security framework that helps manage risk across the broader AI stack, from models and prompts to APIs, dependencies, runtime behavior, and shipped application assets.
We’ll explore what an AI security framework should cover, why traditional controls may not be sufficient for AI-enabled systems, and how to build practical protections across the development lifecycle.
TL;DR
An AI security framework is a practical, lifecycle-wide approach for managing risk in AI-enabled systems. It should cover governance, model, and third-party component risk, data and prompt security, application and binary protection, and runtime controls. Existing references like the NIST AI RMF, the EU AI Act, OWASP’s Top 10 for LLM applications, MITRE ATLAS, and Google’s SAIF are useful, but they serve different purposes and are not interchangeable. For DevSecOps teams, the real challenge is operationalizing those ideas through secure design, CI/CD enforcement, application hardening, and runtime-aware controls. PreEmptive supports the application-layer portion of that strategy by helping teams protect shipped .NET, MAUI, Java, Android, and JavaScript applications against reverse engineering, tampering, and related abuse.
What is an AI security framework?
An AI security framework is an integrated approach designed to protect an organization from the range of risks associated with building, deploying, and operating AI-enabled systems. A mature framework should address governance, privacy, security, third-party risk, runtime behavior, and legal or regulatory obligations.
From an application security standpoint, an AI security framework should help protect data, models, prompts, APIs, application code, dependencies, runtime behavior, and exposed binaries or client-side assets from attack. It is a broad approach designed to keep the full AI-enabled application stack more secure.
In many organizations, AI security started as a narrow review activity. Today, it needs to function as an ongoing engineering practice tied to development, release, and operations.
Why is an AI security framework necessary?
Traditional application security programs are built to address familiar issues such as code defects, exposed endpoints, and vulnerable dependencies.
AI-enabled systems complicate those categories. They introduce risks such as prompt injection, insecure output handling, supply chain issues in model development, and adversarial abuse that do not neatly fit into traditional code-scanning workflows.
Attackers may try to poison training data, manipulate prompts, abuse model outputs, or extract information and behavior through repeated interaction. AI also accelerates development and can speed up attack workflows, increasing pressure on engineering teams to catch issues earlier and respond faster.
SAST and SCA remain essential, but they do not fully address AI-specific risks such as prompt injection, insecure output handling, model misuse, or non-deterministic behavior. Teams need a broader framework that combines scanning, governance, hardening, and runtime resilience.
What should an AI security framework cover?
An effective AI security framework should span the software development lifecycle and be coordinated across functions.
Governance and risk ownership
Risk is best managed when ownership is clear. Engineering teams may own secure integration, API hardening, infrastructure, and delivery controls. Security teams typically lead threat modeling, policy design, and monitoring strategies. Legal, compliance, and privacy stakeholders help determine acceptable use, regulatory exposure, and alignment with internal policies.
Model and third-party component risk
Teams should evaluate model provenance, vendor practices, update cadence, and known limitations before using a model in production. Third-party AI services and components should be reviewed with the same seriousness as other software supply chain dependencies.
Data and prompt security
If an AI system uses sensitive data, the framework should include controls for privacy, data minimization, encryption, and safe prompt handling. Prompt injection and sensitive information disclosure are now established AI application risks, so teams should treat prompts and inference data as assets that need protection.
Application and binary protection
Teams should identify where software functionality depends on AI and decide where application-layer protection is needed. In shipped applications, protections such as obfuscation, anti-tamper measures, and hardening can help reduce reverse engineering and abuse of embedded logic or sensitive workflows. PreEmptive positions its products around this part of the stack for .NET, MAUI, Java, Android/Kotlin, and JavaScript applications.
Runtime and operational controls
Teams should monitor AI-enabled systems for anomalous behavior, misuse, unsafe actions, and policy violations. High-consequence actions should be constrained, and human approval should remain in the loop where needed.
Core principles behind effective AI security
AI security measures are most effective when they are built into the system and reinforced throughout the lifecycle.
Secure by design
During design, teams should define trust boundaries, control access to sensitive data, and restrict high-risk actions. Security is more effective when it is part of architecture and workflow design rather than something layered on after deployment.
Layered defense
AI systems are complex and often non-deterministic, so one control is rarely enough. A strong defense combines governance, scanning, policy enforcement, and application protection.
Lifecycle coverage
Controls should extend from design and build to deployment and operations. That includes model and data review early on, delivery-time security checks, and runtime monitoring after release.
Workflow-friendly enforcement
Protections should work in the way teams already build software. Security controls are more effective when embedded in CI/CD and developer workflows rather than relying on optional manual steps.
Top security risks in AI-enabled applications
AI risks are different from traditional software risks. Here are some of the most important ones to watch.
Prompt injection and instruction manipulation
Attackers can use crafted inputs to alter model behavior in unintended ways. This can lead to data leakage, unsafe output, policy bypass, or misuse of connected tools.
Sensitive data exposure
AI systems can expose private or regulated data through prompts, logs, retrieval workflows, outputs, or integrations. This creates security, privacy, and compliance risk.
Reverse engineering of AI-enabled applications
In client-side, mobile, desktop, edge, or distributed applications, attackers may attempt to reverse engineer binaries to understand internal logic, integrations, or security assumptions. That risk is especially relevant when valuable business logic is shipped with the application. PreEmptive’s positioning centers on reducing this type of exposure through obfuscation and hardening.
Runtime tampering and abuse
Attackers may try to manipulate application behavior through debugging, tampering, or other forms of dynamic analysis. Runtime-oriented protections can help make those attacks harder to carry out successfully.
Uncontrolled tool and agent actions
Many AI agents connect to external systems, such as email, ERP systems, or databases. Without careful boundaries, those agents may be able to perform high-risk actions that should require stronger controls or human approval.
How to structure AI security controls across the development lifecycle
Design and architecture
During design, determine exactly where AI is used and which systems it touches. At this stage, establish trust boundaries, identify high-consequence actions, and map which assets require stronger protection.
Development and integration
During development, review AI-related components, libraries, prompts, and orchestration logic. Teams should also define application protection requirements where AI-enabled logic or sensitive workflows will ship to clients.
Build and release
In the build and release phases, reinforce security checks in the CI/CD pipeline and ensure required protections are consistently applied. If critical controls are missing, teams should stop the build or gate the release.
Deployment and runtime
After release, monitor for policy violations, abuse patterns, and runtime conditions that suggest tampering or misuse.
Ongoing review
Because AI systems and workflows evolve, teams should regularly revisit policies and controls, especially when models, prompts, workflows, or integrations change.
Example: Applying lifecycle security to an AI-enabled application
Healthcare organizations increasingly use AI to help manage patient interactions, route calls, and support operational workflows. In a regulated sector, data governance, privacy controls, and clear operational boundaries need to be built into those systems from the start.
Successful deployments begin with secure design and continue with validation, testing, and ongoing monitoring so the application performs as expected without exposing private data or drifting away from policy expectations.
Common gaps in early AI security programs
AI security frameworks often overlook the following:
- Runtime visibility: Perimeter defenses alone are not enough; teams need visibility into live application behavior.
- Implementation detail: Governance alone is insufficient without concrete implementation plans.
- Application exposure: Organizations sometimes overlook the client-side or shipped application layer that houses AI-enabled workflows.
- Weak CI/CD enforcement: Security checks should be mandatory where they matter, not optional.
Building AI security into DevSecOps workflows
Align scanning, testing, and protection by creating a feedback loop that connects AppSec findings, quality signals, and runtime protection data.
Enforce protection in CI/CD by making security requirements automatic, auditable, and repeatable.
Protect what ships by securing the code and binaries that contain valuable business logic, proprietary workflows, or sensitive integration details.
Prioritize real exposure by focusing on high-value logic, sensitive workflows, regulated data paths, and externally accessible applications.
How PreEmptive strengthens AI security at the application layer
PreEmptive focuses on the application-layer side of security, especially for teams shipping .NET, MAUI, Java, Android/Kotlin, and JavaScript applications. Its product positioning centers on code obfuscation, hardening, anti-tamper protection, and defenses against reverse engineering and dynamic analysis.
Measures such as application hardening, obfuscation, and tamper resistance make applications harder to reverse-engineer or manipulate, which matters when AI-enabled functionality or sensitive workflow logic is exposed in shipped software.
PreEmptive also describes protections against debugging, tampering, and related misuse, and emphasizes workflow alignment for teams securing .NET, MAUI, Java, Android, and JavaScript applications throughout development and release.
Comparison of leading AI governance, security, and risk references
Note: This comparison includes a mix of voluntary frameworks, legal requirements, threat-modeling resources, and ethics guidance. They are complementary but not interchangeable.
| Dimension | NIST AI RMF | EU AI Act | OWASP Top 10 for LLMs | MITRE ATLAS | Google SAIF |
| Framework type → | Governance | Regulatory | Technical security | Threat knowledge base | Security framework |
| Scope | Broad AI risk management across the full AI lifecycle for any organization developing or deploying AI | AI systems placed on the EU market or affecting EU residents, tiered by risk level | Security vulnerabilities specific to LLM applications; developer-focused | Adversarial tactics and techniques targeting ML systems; threat intelligence | End-to-end secure development and deployment of AI systems at scale |
| Risk categorization | Four functions: Govern, Map, Measure, Manage; context-driven, not prescriptive | Unacceptable risk (banned), high risk, limited risk, minimal risk — legally defined categories | Ranked list of top 10 vulnerability classes (e.g., prompt injection, insecure output handling) | Tactic and technique taxonomy (e.g., reconnaissance, evasion, exfiltration) mapped to ML attack stages | Six core principles spanning supply chain, model integrity, detection, and response |
| Implementation approach | Voluntary guidance; organizations self-select applicable practices via profiles and tiers | Mandatory compliance for in-scope systems; conformity assessments, registration, and documentation required | Community-maintained checklist; developers apply mitigations per vulnerability class | Reference knowledge base; teams use it to map attack surfaces and test defenses | Principled framework with recommended controls; voluntary, vendor-agnostic |
| Regulatory nature | Voluntary; no enforcement mechanism | Legally binding regulation with penalties up to €35M or 7% of global turnover | Non-regulatory; open-source community standard | Non-regulatory; freely available threat intelligence resource | Non-regulatory; industry guidance from Google |
| Geographic focus | US-origin, globally applicable | European Union (extraterritorial reach for EU-affecting systems) | Global; no geographic restriction | Global; US-origin (MITRE is a US nonprofit) | Global; US-origin (Google) |
| Stakeholder engagement | Cross-sector: developers, deployers, users, policymakers, civil society | Regulators, notified bodies, high-risk system operators and providers, and affected individuals | Primarily developers, security engineers, and application architects | Security researchers, red teams, threat intelligence analysts, ML engineers | Security teams, AI/ML engineers, and enterprise organizations |
| Adaptability | High; designed for diverse contexts with flexible profiles and tiers | Low for in-scope systems; requirements are legally fixed with limited interpretive flexibility | Moderate; updated periodically by the community; focuses narrowly on the LLM attack surface | High; living knowledge base continuously updated with new adversarial techniques | Moderate; principled but relatively high-level; requires organizational interpretation |
| Security focus | Broad — includes safety, fairness, explainability, privacy, and security holistically | Compliance and fundamental rights-oriented security requirements for high-risk systems | High; deep focus on LLM-specific attack vectors and mitigations | High; exclusively adversarial ML attack taxonomy and defense mapping | High; secure-by-design principles across the AI development and deployment lifecycle |
What can organizations learn from existing AI security standards?
In practice, most organizations use a mix of risk management guidance, regulatory requirements, threat-modeling resources, and technical security frameworks.
Those references are useful, but they do not replace practical implementation. Organizations still need concrete DevSecOps controls that make security real across development, release, and operations.
Best practices for adopting an AI security framework
Effective AI security frameworks start with clear ownership and a realistic risk assessment, including a detailed map of where AI is used.
Controls should be defined and applied across data, prompts, integrations, runtime behavior, and, where applicable, shipped binaries or client-side assets. Security measures should also be enforced in CI/CD wherever possible.
Teams should regularly review the framework to ensure it continues to reflect current models, workflows, and risks.
Bottom line: Implementation separates strong security programs from paper policies
AI security frameworks are most effective when they are built around practical controls, not just governance. The strongest strategies integrate design, AppSec, application protection, and DevSecOps execution into a single, defensible system.
Start a free trial to see how PreEmptive can help strengthen AI-enabled applications at the application layer.
FAQs
What is an AI security framework?
An AI security framework is a structured way to manage the risks of AI-enabled systems across design, development, deployment, and operations. It typically combines governance, security controls, monitoring, and lifecycle practices rather than focusing on a single tool or scan.
How is AI security different from traditional application security?
Traditional AppSec focuses heavily on code defects, dependencies, and exposed services. AI security also has to account for risks like prompt injection, insecure output handling, model misuse, and AI-specific supply chain concerns.
Do existing standards like NIST, OWASP, and the EU AI Act do the same job?
No. NIST AI RMF is a voluntary risk management framework; the EU AI Act is a binding regulation for in-scope systems; OWASP’s LLM guidance focuses on technical security issues; MITRE ATLAS maps adversarial AI tactics; and Google SAIF offers guidance on secure AI implementation. They complement each other, but they are not interchangeable.
Where should DevSecOps teams focus first?
Start by mapping where AI is actually used, who owns the associated risks, which data and prompts need protection, and where enforcement should occur in CI/CD. Then identify which parts of the application layer need hardening or runtime-aware protections.
How does PreEmptive fit into an AI security framework?
PreEmptive fits into the application-layer side of the framework. Its products focus on obfuscation, hardening, anti-tamper protection, and reducing reverse engineering exposure in shipped .NET, MAUI, Java, Android/Kotlin, and JavaScript applications.