The first collaborative database where SMEs and enterprises exchange practical AI risk mitigation strategies. From fake CVs to AI slop, learn from real experiences shared by your peers.
While large corporations have dedicated AI governance teams, SMEs and honest workers are left behind—struggling to identify and mitigate real AI risks in their daily operations.
Companies receive AI-generated applications, but screening processes are too slow and ineffective at detection.
AI capabilities evolve rapidly. Companies fall behind competitors who adapt faster to emerging risks and opportunities.
Academic AI risk databases exist, but aren't designed for practical, day-to-day use by working professionals.
A simple, collaborative approach to AI risk management that puts peer-to-peer knowledge sharing at the center.
Companies and professionals submit AI risks they've encountered in their work—from fake applications to code quality issues.
Members review and validate submissions, ensuring quality and relevance. Peer voting helps surface the most impactful risks.
Search the database by industry, company size, or risk type. Find risks relevant to your specific context and domain.
Apply proven mitigation strategies shared by peers who've solved similar problems in similar business contexts.
Explore the seven major categories of AI risks identified by the community, from regulatory challenges to workforce impacts.
Lack of comprehensive regulation and ethical frameworks
AI-generated misinformation and quality control challenges
Workforce displacement and professional transitions
Privacy concerns and unauthorized data usage
Ethical concerns and behavioral manipulation
Digital divide and inequality amplification
Psychological stress from rapid technological change
Discover actionable strategies shared by companies who've successfully addressed AI risks in their organizations.
Each row summarizes a risk and links it to the paper where it is discussed. Items marked with "(Own synthesis)" contain additional interpretation beyond the original text.
| Category | Context | Description | Source |
|---|---|---|---|
| Lack of regulation for AI/GenAI | General / Business | Unregulated AI markets create ethical, legal and accountability risks, including unclear liability, economic disparities and geopolitical tensions. | Wach et al. 2023 |
| Poor quality, disinformation & deepfakes | General / Business | GenAI can hallucinate, spread disinformation, and enable deepfake content; low information quality can lead to bad business decisions and erode trust. | Wach et al. 2023 |
| Algorithmic bias | General / Business | Models trained on biased data can reproduce and amplify stereotypes (e.g. race, gender), leading to unfair or discriminatory outcomes. | Wach et al. 2023 |
| Automation-spurred job losses | General + Software Engineering | Automation may displace workers and change the job structure; some developer roles are partially or fully replaced while new ones emerge. | Wach et al. 2023; Sauvola et al. 2024 |
| Privacy, surveillance & data misuse | General / Business | Use of GenAI on personal or sensitive data can violate privacy, enable social surveillance, and create legal exposure. | Wach et al. 2023 |
| Social manipulation & weakening ethics | General / Society | Convincing generated text can be misused for propaganda, manipulation and erosion of ethics and goodwill. | Wach et al. 2023 |
| Socio-economic inequalities & AI divide | General / Macro level | Unbalanced access to GenAI, capital and skills can widen inequalities between firms, countries and workers. | Wach et al. 2023 |
| AI-related technostress | General / Organizations | Employees may experience techno-overload, techno-insecurity and techno-uncertainty when GenAI tools are introduced, harming well-being. | Wach et al. 2023 |
| Role changes and pressure on dev processes | Software Engineering | Rapid GenAI adoption redefines roles, tools and operations; existing co-creation processes (e.g. Agile, DevOps) are pressured and must adapt. | Sauvola et al. 2024 |
| Job losses and workforce reshaping in SE | Software Engineering | Certain software development tasks become automated; some roles are replaced or narrowed, while new orchestration and supervision roles appear. | Sauvola et al. 2024 |
| IP ownership, copyright & illegal copying | Software Engineering | Ambiguity over ownership of AI-generated code and the risk of illegal copying/stealing of existing codebases and IP. | Sauvola et al. 2024 |
| Security vulnerabilities & AI-generated malware | Software Engineering | GenAI can help create malware and new attack variants; integrating LLMs into products introduces new security and platform vulnerabilities. | Sauvola et al. 2024; Russo 2024 |
| Code quality, correctness & maintainability | Software Engineering | Generated code can be syntactically correct but semantically wrong, inefficient, or hard to maintain; developers spend time reviewing and debugging AI output. | Russo 2024 |
| Workflow misalignment & adoption friction | Software Engineering | If GenAI tools are incompatible with existing workflows, they cause friction, extra coordination effort, and can reduce perceived usefulness. | Russo 2024 |
| Bias and harmful outputs in software features | Software Engineering | When LLMs are embedded into user-facing features, biased or harmful outputs can propagate directly to end-users, affecting safety and reputation. | Russo 2024; Wach et al. 2023 |
Concrete actions organisations can take to manage GenAI risks. Rows marked with "(Own synthesis)" generalise or extend the recommendations from the papers.
| Action / Control | Target | Description | Source |
|---|---|---|---|
| Establish AI governance & align with regulation | Organization / Leadership | Set up AI governance structures that define responsibilities, risk appetite and compliance with emerging AI regulations and standards. (Own synthesis) | Wach et al. 2023 |
| Implement quality control & fact-checking for AI outputs | Organization / Product teams | Use diverse, pre-approved datasets, human feedback loops, fact-checking mechanisms and monitoring to control information quality and reduce disinformation. | Wach et al. 2023 |
| Define responsible AI principles and ethics guidelines | Organization-wide | Adopt ethical guidelines including transparency in data usage, bias mitigation techniques and monitoring of generated content for harmful or misleading information. | Wach et al. 2023 |
| Strengthen privacy-by-design & data protection | Security / Data governance | Prioritize user privacy and security in GenAI design, data collection and processing; clarify data ownership and minimize personal data use. | Wach et al. 2023 |
| Invest in reskilling and continuous training | HR / Engineering management | Provide ongoing digital-skills training and reskilling so workers can adapt to changing jobs and new GenAI-related roles. | Wach et al. 2023; Sauvola et al. 2024; Russo 2024 |
| Mitigate technostress with policies and culture | Management / HR | Offer adequate training and support, communicate capabilities and limitations of GenAI clearly, and create work-life-balance policies that avoid constant connectivity. | Wach et al. 2023 |
| Create new roles for AI supervision and optimization | Engineering org design | Introduce roles for AI system supervision, optimization and oversight to orchestrate AI-driven development and ensure accountability. (Own synthesis) | Sauvola et al. 2024 |
| Embed cybersecurity & secure-by-design for GenAI | Security / Engineering | Treat cybersecurity as a design issue: pre-emptive and preventive controls for LLM misuse, malware generation and platform vulnerabilities, embedded into the SDLC. | Sauvola et al. 2024; Russo 2024 |
| Run structured AI tool adoption (requirements → pilots → rollout) | Engineering / Product | Conduct requirement analysis, resource evaluation and constraint identification; then do pilot tests, evaluate impact and scale only when tools fit workflows well. | Russo 2024 |
| Prioritize workflow compatibility when selecting tools | Tooling / Platform teams | Choose GenAI tools primarily on how well they integrate with existing development workflows, as compatibility is a key driver of adoption. | Russo 2024 |
| Set up feedback loops & stakeholder dialogues | Organization-wide | Create feedback mechanisms for developers and users, and hold open dialogues with stakeholders (including regulators) about security and ethical issues. | Russo 2024 |
| Address global and internal AI divides | Policy / Strategy | Support access to skills and infrastructure, and use financial and cooperative instruments to avoid leaving regions or groups behind. (Own synthesis) | Wach et al. 2023 |
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Everything you need to know about the platform
All submissions go through peer review by community members. Contributors can vote on the relevance and accuracy of risks and solutions, ensuring only high-quality, validated content makes it into the main database.
We recommend anonymizing all submissions. Share the nature of the risk and how you mitigated it without revealing proprietary details, client names, or sensitive data. Enterprise plans offer private workspaces for internal collaboration.
SMEs, startups, enterprise teams, AI practitioners, compliance officers, and anyone responsible for managing AI risks in their organization. If you're deploying AI or dealing with AI-related challenges, this platform is for you.
While academic databases focus on theoretical risks, our platform emphasizes practical, real-world experiences from working professionals. Solutions are crowdsourced from people who've actually implemented them, not just theoretical recommendations.