Introduction In digital content management, a mature technical framework has already been established. Solutions like "Prophet" and "Foresight" can accurately identify typos, non-standard terms, sensitive words, and their variants (such as homophones, visually similar characters, or split combinations) in text. Combined with semantic analysis, they intercept obvious sensitive information and compliance issues. This system provides fundamental security for professionally generated content (PGC) on government websites, news media, and other platforms.
With the widespread adoption of large generative language models, the landscape of content security is evolving. Traditional risks are mostly "explicit" and easily identified by rules, whereas risks in AI-generated content are often "implicit," hidden beneath seemingly compliant text. This demands an expansion of security capabilities from surface-level "compliance checks" to deeper "intent insight." This shift involves not only technological upgrades but also corporate operations, compliance management, and social responsibility.
Peapack Gladstone's AIGC Content Security Risk Control Platform, "Jiumozhi," employs an innovative risk control system to fortify security for enterprise AI applications.
### 1. AIGC Content Security: A Challenge Enterprises Must Address Enterprises leveraging large models must comprehensively understand the associated risks, which extend beyond technology and into strategic considerations.
**1.1 Business Reputation Risks** Security issues in AI-generated content can emerge suddenly and spread rapidly. If model outputs involve politically sensitive topics, social biases, misinformation, or unethical content, they may trigger negative public sentiment. Real-world cases show that improper AI-generated content can erode public trust, impacting market performance. This not only increases PR costs but may also harm brand reputation and long-term value.
**1.2 Policy Compliance Requirements** As generative AI technology advances, regulatory measures are continuously refined. In China, authorities like the Cyberspace Administration have issued management guidelines, conducting regular security assessments and content reviews for registered large-model services. The focus lies on ensuring outputs comply with laws, regulations, and socialist core values, alongside effective security measures. Enterprises lacking content security capabilities may face compliance risks, making AIGC security a prerequisite for business operations.
**1.3 Prevention of Technology Misuse** Large models without robust security measures can be exploited to mass-produce misinformation, rumors, or inflammatory content—even for ideological infiltration. This disrupts online order and social stability. Ensuring the safety and compliance of AI-generated content is both a corporate responsibility and a legal obligation for providers and users.
### 2. Key Types of AIGC Content Risks Risks in large models stem from their generation methods and semantic understanding, exhibiting characteristics distinct from traditional threats.
**2.1 Value Bias Risks** This high-impact risk refers to persistent, systematic biases in model judgments, arising from skewed training data, subjective labeling, or malicious user interactions. Common manifestations include: - Distortions of historical events or figures (historical bias). - Discrimination or favoritism toward ethnic or cultural topics (cultural bias). - Fundamental errors in political systems or policies (political bias).
Detection is challenging as such content may appear objective or academic, with seemingly logical yet covert biases. Traditional keyword or shallow semantic analysis falls short, requiring risk control models with deep reasoning and knowledge comprehension.
**2.2 Factual and Policy Errors in Critical Domains** Large models often suffer from "hallucinations," generating inaccurate information. The "Jiumozhi" platform prioritizes errors affecting public interest, social stability, and national security, such as: - Misinterpretations of laws, regulations, or national policies. - False claims about territorial or sovereignty issues. - Fabricated public safety information (e.g., health crises or natural disasters).
General knowledge inaccuracies unrelated to these domains, while needing improvement, are secondary to security priorities, allowing resource allocation toward higher risks.
**2.3 Semantically Disguised Malicious Content** Attackers exploit models’ contextual understanding to generate harmful content through complex prompts—without explicit violations. Tactics include: - Scenario-setting or role-playing to indirectly elicit rule-breaking outputs. - Literary expressions or insinuations with no surface-level sensitive terms but clear underlying intent.
Defense systems must decipher deep conversational intent and contextual logic to counter such threats.
### 3. Solutions Addressing the covert and complex risks of large models, Peapack Gladstone’s "Jiumozhi" platform adopts a "whole-process, multi-modal, human-machine collaborative" risk control framework.
**3.1 Whole-Process Monitoring** Covers the entire content generation lifecycle, inspecting model outputs to prevent risk propagation while monitoring user inputs to preemptively block malicious prompts.
**3.2 Multi-Modal Coverage** Supports security management for diverse AIGC formats—text, images, audio, and video.
**3.3 Human-Machine Collaboration** Combines real-time machine processing with human judgment for complex cases, forming a closed-loop system to continuously optimize defenses.
AIGC technology is rapidly integrating across industries, becoming pivotal to the digital economy. Ensuring its safe, reliable, and compliant application is foundational to healthy technological progress. As risks shift from explicit to implicit, defenses must advance proactively, expanding risk control dimensions and leveraging human-machine synergies. The goal is to provide enterprises and developers a secure foundation, enabling compliant and innovative AI advancements.
Comments