On December 8, 2025, around hundred parliamentarians in the UK have called for stronger AI safety initiatives by the government. AI safety is a discipline that is evolving at a speedy pace to reduce existential crisis.
Do you know that artificial intelligence technology can harm society more than evolving it? Explore the need for AI safety, its standards, frameworks, principles, tools, and techniques.
Don’t forget to check out how the United Kingdom and other major countries are contributing to AI safety research.
What is AI Safety and its main principles?
It involves the various security principles and techniques to make sure that AI tools or models are secure and efficient. AI safety ensures that businesses and industries aren’t becoming the victims of the negative impacts of artificial intelligence.
The purpose of this field is to spot the major AI risks and build tools and techniques for their mitigation. It is crucial to build a safe AI environment since it is embedded in industries like business, e-learning and finance.
According to AI ethical policies, the development of artificial intelligence models and systems focus on the following principles:
AI Robustness
The purpose of producing robust AI systems is to ensure model reliability and security. AI developers focus on a rigorous model testing and validation process to identify and eliminate vulnerabilities.
Experts use security techniques like anomaly detection and redundancy in AI systems to protect them from adversarial attacks.
AI Alignment
AI Alignment is a safety principle that ensures that the AI systems are designed with alignment to human morals and behaviours. The AI professionals build systems that implement the human goals in their operational framework for producing a positive impact.
It is significant to regularly monitor AI systems after implementation to ensure that they are aligned with human objectives.
AI Transparency
Transparency is an important element of an AI system that focuses on the development of understandable and value-driven outcomes. Model interpretability is a technique that is often used to achieve AI transparency even for systems with complex algorithms.
AI Accountability
AI accountability is the principle that ensures that an artificial intelligence system is socially and ethically safe. Moreover, this principle includes a set of security frameworks and regulatory standards to define the responsibilities of AI companies.
Regular compliance checks and security audits are required to achieve accountability in AI systems.
What is the importance of AI safety?
AI safety is significant since artificial intelligence is quickly integrating within everyday life. According to statistics, around 83.1% of AI models are victims of algorithmic bias which is negatively affecting the educational sector.
Moreover, 80% data experts reported that artificial intelligence has raised serious data privacy concerns. Businesses and industries need transparent, fair and unbiased AI systems that don’t contribute to any form of harm.
The rise of Artificial general Intelligence technology that thinks and learns like a human has caused great existential risks. Moreover, Artificial Superintelligence technology that has better cognitive abilities than humans is required to be controlled with strict security frameworks.
Apart from this, AI safety is integral for businesses to protect their reputation and consumer’s trust. Since, according to the McKinsey report around 88% of businesses are actively investing in artificial intelligence for enhanced decision-making.
What is the difference between AI security and AI safety?
The purpose of AI safety is to make sure that AI applications matched with human values and are reliable. Meanwhile, AI security is focused on safeguarding AI systems from threats or cyber-attacks.
Moreover, the discipline of AI security is focused on using machine learning for the protection of an organisation’s security infrastructure.
What are the major AI risks?
Artificial intelligence safety experts identify AI risks that are harmful for industries before discovering solutions for their mitigation.
Here’s a list of AI risks that affect industries and humanity:
Misuse of AI
Malicious or bad actors are also investing in the use of AI to conduct massive and sophisticated cyber-attacks or terrorist activities. The misuse of AI includes security threats, physical security incidents, illegal activities and rise of misinformation through data poisoning.
Existential Risks
Lack of control over sophisticated technologies including ASI and AGI can be harmful for the whole of humanity. AI ethical experts have also raised concerns regarding large-scale terror or cybercrimes with the help of AI.
Algorithmic Bias
This AI risk involves the use of discriminatory data against a specific race or ethnicity to form biased decisions. AI bias is one of the biggest limitations when considering the adaption of AI tools and systems.
Lack of Privacy
Lack of data privacy is a serious issue that involves the misuse or exposure of sensitive user data by AI. It is a major security risk since businesses can lose their customer’s trust in case of a data breach.
Moreover, such situations can also result in non-regulatory behaviours, holding AI companies and developers responsible.
Loss of Control
Businesses are investing in AI agents that work anonymously without any human intervention. However, losing control over such autonomous agents can be risky in terms of decision-making.
Lack of Security
Reduced security from adversarial attacks can be dangerous for AI systems since they can be easily manipulated with data poisoning.
What are the common safety measures used by AI safety experts?
Businesses and AI startups or companies should focus on the following safety techniques and measures for safe AI systems:
Human Intervention
Human-in-the-loop is an important safety policy for AI systems to ensure accountability. Businesses are getting dependent on autonomous agents that can operate without any human monitoring. Gartner reports that in 2026 around 40% of businesses will be using AI agents.
However, human intervention can save organisations from security disasters by monitoring and iterating such AI systems. Moreover, it also helps with enhanced and critical decision-making for businesses.
Ethical AI Models
It is important to have ethical guidelines and security rules for building ethical AI models. Organisations should ensure that their AI systems and tools abide by principles like fairness, transparency and accountability.
Security Frameworks
Businesses should invest into strong security measures like anomaly detection, threat detection, access control and more. These security frameworks are significant for protecting systems and tools from unauthorised access, cyber threats and AI bias.
Explainable AI
There are several AI models known as black boxes that have a complex decision-making process which is beyond human cognitive ability. This affects the transparency of the AI models and stakeholders don’t trust the AI outputs.
Explainable AI (XAI) can be used to explain the results of an AI output with the help of model interpretability. This safety measure is significant to ensure the transparency of the decision-making process by artificial intelligence algorithms.
Bias Mitigation
Algorithmic bias is a constant issue for businesses who are using artificial intelligence in their workflows. It is important to use techniques like algorithmic fairness and various datasets to mitigate the biasness from AI systems.
AI model Testing
Model testing and validation is an important safety measure to free AI systems from any form of vulnerability. To ensure that the AI systems are working efficiently, experts use stress testing and adversarial testing.
Industrial Collaboration
Artificial intelligence safety is a new field and requires industry-wide collaboration to ensure reliability of advanced systems. Researchers, businesses, developers, policymakers should collaborate to build AI systems that are safe, transparent and ethical.
What are some AI safety standards and frameworks?
Here’s a list of some AI safety standards and regulatory frameworks that AI companies are bound to follow:
AI Risk Management Framework by NIST
This framework is developed by the National Institute of Standards and Technology to identify and mitigate AI risks. It also stressed on applying a comprehensive strategy for risk management to ensure that AI operations are reliable and secure. The framework has also recommended strategies for mitigating AI risks, AI testing and assessments as well as involving stakeholders.
OCED Artificial Intelligence Principles
The OCED framework was initially introduced in 2019 and updated in 2024 according to the advancement in artificial intelligence. It focused on providing AI policies and ethical guidelines for the production of trustable systems.
Moreover, it safeguards human rights and democratic values with the development of accountable and transparent AI models. It also stresses on global co-operation and interoperable governance for artificial intelligence.
IEEE Framework for Ethical AI
The IEEE framework offers a list of ethical guidelines that ensure the alignment of autonomous systems with human rights and values. It focuses on the production of human-centric AI systems that are transparent, accountable, ethical, and fair.
Moreover, it is a regulatory guideline for AI developers to ensure the reliability of their creations throughout the development phase.
ISO/IEC Artificial Intelligence Framework
The ethical frameworks by ISO for the use and production of artificial intelligence applications include compliance audits and impact assessments. Moreover, it focuses on achieving risk mitigation, data privacy, and fairness within AI systems.
Some of the popular ISO/IEC AI Frameworks include:
- ISO/IEC 22989
- ISO/IEC 5338
- ISO/IEC 23894
- ISO/IEC 42001
- ISO/IEC 42005
Google Secure AI Framework
The framework by Google ensures that AI systems are developed, tested and validated by the best security protocols. Moreover, it involves techniques like threat modelling and AI monitoring to spot and mitigate vulnerabilities.
By introducing this framework, Google makes sure that user data is protected and prioritised throughout the development of AI systems.
What are the AI safety techniques and tools?
AI companies invest in the following techniques and tools to achieve AI safety:
Red Teaming
Red teaming techniques are used to protect users from potential harm by conducting stress tests and risk assessments. The red team members protect AI systems from data poisoning, prompt injection, jailbreaking, and model inversion.
Here’s a list of AI safety tools used for red teaming:
- AI Fairness 360 by IBM
- Garak by NVIDIA
- Foolbox
- Guardrails-AI
- Counterfit by Microsoft
AI Alignment
This AI safety technique involves the use of strategies to align AI systems with human values and goals. Developers use model training tools and techniques like:
- Reinforcement learning from human feedback (RLHF)
- Synthetic data generation
- Reinforcement learning from AI Feedback (RLAIF)
Data Privacy
It ensures the use of data anonymisation, encryption and differential privacy to protect personal user data from AI algorithms. AI companies use following tools for protecting sensitive user data:
- OvalEdge
- BigID
- IBM Security Guardium
Interpretability
AI companies invest in model interpretability to ensure compliant, transparent and trustable AI systems. The developers focus on building explainable AI models with the following tools:
- Local Interpretable Model-Agnostic Explanations (LIME)
- Partial Dependence Plots (PDP)
- SHapley Additive exPlanations (SHAP)
Guardrails
Experts use AI guardrails that ensure that the behaviour of an AI system is aligned with the defined guidelines. There are three types of guardrails such as input, output and processing guardrail.
Here’s a list of AI guardrails tools:
- EdenAI
- Purple Llama
- Guardrails AI
AI Monitoring
AI monitoring is a safety technique used to analyse AI models proactively to maintain their health and track suspicious behaviour. AI experts use the following model monitoring tools:
- WhyLabs by IBM
- Fiddler AI
- Instana by IBM
- Arize AI
- Comet ML
What are the stakeholders involved in AI safety research?
Let’s explore the stakeholders who are involved in making the artificial intelligence technology safe for the entire humanity.
AI Development Companies
AI development companies like Anthropic, OpenAI, DeepMind, Microsoft, Google and others are the prominent stakeholders. These companies are ensuring the ethical use of AI systems with safety research teams and ethical guidelines.
The AI companies are investing into tools and techniques to ensure safety during the development and deployment phase. Moreover, they’re also co-ordinating with businesses and industries to encourage and support the safe use of AI.
AI Researchers and Developers
The developers and researchers play a big role in the development of safe AI systems. These stakeholders focus on building clear and explainable AI (XAI) models. Moreover, they achieve the alignment of AI systems with human values with the help of model iteration.
The developers also ensure the reliability of an AI system or model by testing and validation.
Non-profit Organisations
The researchers from non-profit organisations and government-level policymakers focus on the alignment of AI with moral and safety guidelines. These institutes stress on human rights, legislative and security concerns and the related impact of AI technology.
Moreover, these stakeholders also address severe AI risks and build ethical guidelines for the regulation of AI systems. Following are some significant organisations and advocacy institutes:
- Future of Life institute: The aim of this institute is to control and reduce the existential risks connected with the usage of AI. They’ve also released an AI safety index to address this AI risk.
- Centre for AI Safety: Also known as CAIS, is a non-profit organisation invested in introducing learning material to address AI risk concerned with society.
- AI Safety Initiative: It is established by Cloud security alliance to develop tools for the ethical and secure deployment of AI systems.
- Partnership on AI: It is a collaborative work by some industry and educational institutes to produce research and ethics for responsible AI usage.
- Centre for Human-Compatible AI: Popularly known as CHAI, this institute is responsible for producing research for value learning and reinforcement learning.
- Human-centred Artificial Intelligence by Stanford Institute: The work of this organisation involves research and advocacy to ensure AI alignment with human objectives.
AI Regulators and Governments
The state-level AI governance policies are crafted by governments and other policymakers like World Economic Forum and OCED and UN. These stakeholders are focused on developing regulatory frameworks to ensure AI safety.
Following are some of the AI regulatory and governance laws for ethical usage and development of AI systems:
- Ethics Guidelines for Trustworthy AI by EU: This security framework focused on the ethical deployment of AI models or tools. Moreover, it also stressed on human intervention to ensure reliability and robustness of an AI system.
It also included regulatory rules like transparency, fairness and data protection. The European Union has created this guideline to ensure that AI systems are ethical and safe for humanity.
- National Artificial Intelligence Initiative Act by US: This act was introduced with the intention of encouraging advanced research in the field of artificial intelligence. The US government supported and funded the projects of AI development with this act and also ensured their reliability.
Moreover, it also encourages global collaboration for the establishment of secure AI systems for humanity.
- Artificial Intelligence Ethics Framework: This ethical framework was introduced by Australia’s regulatory body ACCC. The purpose of proposing this framework was to ensure the development of explainable, robust and accountable AI models and systems.
Moreover, the purpose of the Australian government was to encourage the production of inclusive systems that don’t exploit consumer rights.
- EUs Artificial Intelligence Act: It is a legislative framework which was introduced to ensure the protection of consumer rights and personal data. Europe introduced this ethical framework to test AI applications and systems on the basis of transparency, existential risks and data privacy.
The AI act also ensures bias mitigation with the use of datasets that are inclusive. Moreover, it also stressed on the fact that consumers should be aware of their interactions with AI systems.
- Algorithmic Accountability Act (AAA): America introduced this test to ensure algorithm safety in automated AI systems. According to this act, AI companies are required to rigorously test their products for bias, transparency and accountability.
Moreover, the act also advised the AI development companies to disclose their procedures, datasets for AI training and societal impacts.
AI Safety Initiatives by the UK and other Countries
The United Kingdom and other countries have taken the following security initiatives to enhance AI safety:
AI Security Institute (AISI)
The AI Security Institute (AISI) formerly known as AI safety institute was established to enhance protection while using Frontier AI models. The institute was developed in 2023 when the discipline gained prominence due to Rishi Sunak’s AI regulation policy.
The institute rebranded itself in February 2025 to focus on its role as a network co-ordinator. The network was created during the 2024 safety summit in South Korea, including the following countries:
- United Kingdom
- Australia
- Europe
- Canada
- Japan
- France
- United States
- Kenya
- Singapore
- South Korea
The purpose of this institute is to focus on advancing AI research and co-ordinating with the members of the network. Moreover, the institute is also developing AI infrastructure and enhancing risk management by discovering and mitigating issues.
AI Safety Summits
The first AI safety summit was organised by the United Kingdom in Bletchley Park during the year 2023. The summit was an international conference that discussed the issues, risks and safety regulations related to artificial intelligence.
It was an initiative by the prime minister Rishi Sunak according to his government policy regarding AI. Moreover, around 28 countries joined the summit which concluded with Bletchley Declaration.
The Bletchley Declaration focused on the generation of AI systems that are human-centred, secure and reliable. Moreover, the summit also discussed the role of Frontier AI, existential risks and terrorism.
Personalities like Sam Altman, Elon Musk, Kamala Harris and more were a notable part of the 2023 summit.
The second safety summit is known as the Seoul Summit which was arranged by South Korea and the United Kingdom. This summit was held in Seoul between May 21 and 22 in 2024.
During the second summit the attendees discussed the significance of artificial intelligence. Moreover, there was also a shared resolution to focus on the identification of major AI risks.
The third safety summit for artificial intelligence was arranged by France and India during February 10 and 11 in 2025. Around 58 countries took part in this summit to ensure the solutions for greater AI risks.
The 2025 summit is known as AI action summit since it discussed the solutions to tackle and mitigate emerging AI risks. Moreover, the conference also focused on the worldwide economic progression with the help of artificial intelligence.
International AI Safety Report
The international AI safety report was an initiative by the members of the 2023 safety summit. It was published in early 2025 and led by Yoshua Bengjo.
The report focused on the capabilities of artificial intelligence that are advancing at a speedy pace. Moreover, it also focused on three types of AI risks including systemic risk, malicious use, and technical failures.
Arena AI Safety Institute
Arena is an AI research institute that is focused on the power of artificial intelligence and concerns like existential safety. The institute wants to reduce AI risks with enhanced research on LLMs, model interpretability and adversarial security.
They also offer bootcamp courses to contribute to the research in the field of AI-alignment.
Cambridge AI Safety Hub
The Cambridge safety institute involves researchers, students and professionals in the artificial intelligence field to ensure reliability and safety. They offer research programmes, workshops, AI training and more.
Moreover, the institute also offers machine learning bootcamps, fellowship opportunities in AI alignment and community events.
AI Safety Index
The Future of Life institute released their 2025 Winter scorecard on artificial intelligence safety. According to the co-founder Max Tegmark, all the big AI models are low scorers when it comes to safety.
The safety index scorecard ranks the AI models according to various factors including:
- Current Harms
- Risk Assessment
- Existential Safety
- Safety Frameworks
- Governance and Accountability
- Information Sharing
According to the safety index, Anthropic and Open AI received a C+ while DeepMind by Google scored C. Moreover, AI models like Z.ai, xAI, DeepSeek, and Meta got a D in overall safety grading.
However, apart from Anthropic, DeepMind and OpenAI all the other AI models received an F for the existential safety factor. According to Sabina Nong who is an AI investigator at the institute, “Every AI model requires safety enhancement”.
Frontier AI Regulation
Chris Lehane, the Chief global affairs officer of the AI company Open AI shared his stance on Frontier AI regulation. He commented on LinkedIn regarding the US regulatory approach for such AI systems for enhanced safety.
He also stated that systems for testing frontier AI models are accessible for the federal government only. He shared that Open AI has already collaborated with the federal government for testing such models.
Moreover, Chris focused on the capability of the government to produce proactive AI models instead of being reactive to harm. He appreciated the regulatory laws in many states and also raised concerns regarding some ineffective ones.
Lastly, he advised three frameworks to save AI startups from unnecessary regulatory laws and expenses. Following are the frameworks:
- He advised the government to allow frontier AI models’ testing through the Centre of AI Standards and innovations (CAISI).
- The second advised framework stated that the US states should align their frontier regulatory requirements with government-level testing.
- The third framework states that AI companies who took part in CAISI testing should be free from frontier regulatory requirements.
Industry-wide best practices for strengthening the safety of AI
To secure industries and ensure the safe use of AI systems, follow these practices:
Constant Monitoring and Iteration
The constant monitoring and iteration of AI systems is crucial to maintain their reliability. Regular assessments and the use of feedback loops help experts in analysing the algorithmic behaviour.
Professionals use the behaviour analysis to ensure alignment with human objectives and for identifying and eliminating vulnerabilities.
Secure AI Development
It is important to ensure the security and reliability of an AI system during the entire development lifecycle. AI developers can use security testing, code testing and more to protect the system with the right security protocols.
Safe Model Training
The artificial intelligence models require a certain amount of data for training its algorithms. It is important to ensure that the model is training on indiscriminatory and clean data.
Experts can use security guardrails and the use of risk assessments to avoid the use of unreliable or discriminatory data.
Efficient Incident Response Planning
Organisations should prepare thorough incident response plans to deal with data breach or security incidents. Effective and pre-made plans can help organisations in reducing down-time and maintaining system integrity.
User Guidance and AI Education
Businesses, financial, healthcare, educational and other sectors should focus on providing comprehensive AI training to their staff and users. The user guidance ensures that a user is educated about the ethical practices and risk identification while using AI systems.
Regular Compliance Audits
The regular security audits and compliance checks helps in maintaining the usability and safety of an AI system. Experts can use the compliance checks to figure vulnerabilities, misalignment and areas of non-compliance.
Use of Inter-disciplinary Teams
It is important to involve a variety of skilled experts in the development phase of an AI system. The professionals from fields like psychology, law and governance can help in refining the AI systems through extensive perspectives.
Advanced Data Anonymisation
Many AI startups and companies are investing in advanced data anonymisation to ensure the security of sensitive data. AI experts use data protection techniques like synthetic data generation to maintain anonymity during model training or operations.
Frequently Asked Questions
Q. What are AI safety concerns?
The common AI safety concerns include lack of fairness, data protection, and model accountability. Moreover, it also involves the misuse of artificial intelligence for terrorism, cyber-attacks and development of modern weaponry.
Q. What is the biggest risk with AI?
Existential risks are among the biggest risks after the wide use of artificial intelligence in various industries. Moreover, technologies like Artificial general intelligence (AGI) and Artificial superintelligence (ASI) are alarming.
According to the AI Safety Index, many AI models including xAI, DeepSeek, Meta AI and Alibaba Cloud ranked F.
Q. What are some AI safety jobs?
The AI security teams are trying to ensure the safety of AI by employing a variety of skilled professionals including:
- AI Researchers
- AI Developers
- AI Trainers
- AI Ethics Specialists
- AI policymakers
- Red Team Specialists
- Security Engineers
Individuals can become a part of the AI safety field by searching for jobs on platforms like LinkedIn, Glassdoor and Indeed. Moreover, the AI Security Institute in the UK also offers various job opportunities.
