Each wave of technological hype introduces new security challenges – and artificial intelligence is no exception. While AI governance might initially seem like unexplored territory, it’s fundamentally an extension of long-established security principles that AppSec teams have been leveraging for years. The core pillars – secure coding, risk management, compliance, and policy enforcement – remain as relevant as ever.
Organizations that already practice secure development, enforce access controls, and adhere to compliance standards are in a strong position to manage AI-related concerns. The oversight of modern AI technologies – such as inference providers, datasets, retrieval-augmented generation (RAG), and autonomous agents – can be effectively integrated into current security and compliance processes. Governing AI-powered solutions shares many similarities with the governance of traditional, non-AI applications. In fact, Mend.io team are already routinely applied to automation tools and machine learning systems used in areas like fraud prevention, bot detection, and anomaly recognition.
The goal isn’t to reinvent the governance wheel, but rather to refine and adjust existing best practices. Secure development lifecycle (SDLC) frameworks, risk evaluation processes, and policy enforcement strategies don’t require a radical rework. Instead, they need to expand to include AI as an integral aspect of software security.
However…
Although AI governance aligns with broader security governance approaches, it introduces a few distinct challenges that differ from what has been encountered previously, including:
- Unpredictable Model Behavior. Traditional software behaves deterministically, whereas AI models can produce variable outcomes for identical inputs – or even generate “hallucinated” responses that diverge from factual accuracy.
- Data Leakage Risks. Applications powered by AI may inadvertently reveal sensitive or proprietary information, especially if trained on confidential datasets or deployed without appropriate safeguards.
- Adversarial Manipulation. Malicious actors can exploit AI’s weaknesses by poisoning training data or crafting input prompts designed to bypass security protocols – something conventional rule-based systems are less vulnerable to.
- Regulatory Uncertainty. Existing compliance frameworks like SOC 2 and ISO 27001 offer solid governance foundations, but AI-specific regulations are still emerging, requiring a flexible and responsive approach.
- Unique AI Licensing Models. Many AI models and their associated weights are openly accessible – but these licenses differ notably from traditional open-source agreements.
These issues, while complex, are manageable. Most can be addressed using the same security-first, shift-left strategies that AppSec professionals are already familiar with. Though AI may represent a newer category of technology, securing it remains consistent with the iterative evolution of current governance practices.
If the scope of AI governance seems overwhelming, start by locating instances of shadow AI within the codebase and beyond. Without awareness, there can be no protection or oversight.
Once identified, these high-level concerns should be mapped to existing AppSec frameworks.
Data Governance and Privacy Controls
- Prevent AI models from training on confidential or proprietary data to avoid unintended leakage.
- Apply role-based access controls (RBAC) to AI tools involved in sensitive security operations.
- Audit logs of AI inputs and outputs regularly to monitor for possible data exposure.
AI Model Security and Monitoring
- Defend against adversarial inputs by validating model interactions against known attack patterns.
- Perform ongoing testing to detect model drift or regressions in security performance.
- Leverage explainable AI (XAI) methods to improve transparency and accountability in AI-driven decisions.
Regulatory Compliance and Policy Enforcement
- Align AI-related security policies with existing standards such as SOC 2, ISO 27001, and GDPR.
- Keep comprehensive documentation of AI decision logic to support compliance verification.
- Stay informed about emerging AI-specific regulations and update governance policies accordingly.
AI Supply Chain Security
- Assess third-party AI components for security vulnerabilities prior to production deployment.
- Use hashing and digital signature mechanisms to validate model integrity and guard against tampering.
- Implement provenance tracking to maintain transparency over the origin and lifecycle of AI artifacts.
AI Risk Assessment and Incident Response
- Integrate AI-specific threats into established threat modeling and risk assessment processes.
- Develop dedicated incident response plans tailored to AI system failures or misconfigurations.
- Conduct red teaming to simulate adversarial scenarios targeting AI-powered environments.
Ethical AI and Bias Mitigation
- Frequently evaluate AI models for fairness and bias, especially in contexts involving security decisions.
- Set up ethics review boards to assess the use of high-risk AI applications.
- Where applicable, ensure end-users have visibility into and influence over security decisions made by AI.
Each of these governance strategies builds upon the existing best practices in AppSec. Together, they reinforce the understanding that securing AI is not a reinvention – it’s a continuation of the proven governance models already in place.
