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Clayton Rifkind

About Clayton Rifkind

https://www.airisktoday.com/

Clayton Rifkind is the Founder and Senior Editor of AI Risk Today. He also advises on business development for ESG Today, a leading source of ESG investment news and research for institutional investors and corporate leaders. He has 20+ years of experience in B2B technology, leading strategy and execution of go-to-market plans across software, enterprise platforms, and mobile applications. He founded two consultancies advising startups and Fortune 1000 companies, including Autodesk, Intel, and Microsoft. He began his career in the San Francisco advertising scene working with brands such as Hewlett-Packard, Intel, Microsoft, Symantec, and Wells Fargo. Clayton launched AI Risk Today in 2025 after two decades of watching enterprises adopt transformative technologies, and seeing how often risk, governance, and compliance considerations lagged behind. His reporting draws on primary sources including regulatory filings, court documents, and official announcements, with a focus on what AI developments mean for the executives accountable for managing them. Reach him at Reach him at [email protected] or on LinkedIn.

Posts by Clayton Rifkind:

Anthropic CEO Dario Amodei speaking onstage at TechCrunch Disrupt 2023.

Anthropic Rejects Open-Weight AI Ban, Calls for Mandatory Safety Tests

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Anthropic CEO Dario Amodei says the most capable open and closed models should be tested before release, regardless of where they were developed.

 

Anthropic says the United States should not ban open-weight AI models, publicly available AI systems anyone can use, and should require mandatory safety testing for the most capable AI models, whether open or closed, before release.

Anthropic’s position differs from parts of a July 24 open letter signed by 77 AI companies that urges U.S. policymakers to preserve access to open-weight models, arguing that they increase competition and give companies more control over their AI systems. Anthropic and OpenAI were noteworthy exceptions that did not sign the letter.

Amodei said he agrees with those benefits. However, he disputed the letter’s argument that broad access to powerful models necessarily helps defenders more than attackers.

Why Anthropic Wants Mandatory Testing

Once a developer releases a model’s core files, it cannot recall every copy or monitor how the model is being used. People can also remove its safeguards and run modified versions privately.

Anthropic argues that this loss of control creates additional risk when a model becomes capable of assisting with attacks.

Mandatory safety testing would examine those capabilities before release. Anthropic wants the requirement to cover the most capable open and closed models, rather than treating open-weight AI models as a separate category.

Amodei advocates that less capable models wouldn’t need to adhere to the safety requirements. He did not specify the level of capability that would trigger testing, who would conduct it, or how the requirement would be enforced.

Anthropic Proposes Other Restrictions

Anthropic also wants the United States to keep powerful AI chips and chipmaking equipment out of China while doing more to stop smuggling and other ways companies evade existing restrictions. Amodei argues that China cannot build models more powerful than leading U.S. systems without access to advanced U.S. chips.

Separately, Anthropic called for stronger action against large-scale model distillation. The company says Chinese AI developers can use thousands of fraudulent accounts to collect millions of responses from advanced U.S. models, then use those responses to improve their own systems faster and at a lower cost than developing the same capabilities independently.

Anthropic argues that this process allows Chinese developers to make better use of their limited computing resources, partially weakening the effect of chip restrictions. However, large-scale distillation still requires advanced chips, so Anthropic says tighter chip controls would also limit how extensively companies can use the technique.

Dark digital illustration of connected laptops representing ChatGPT at work across traditional job boundaries.

OpenAI Says Workers Use ChatGPT Across Traditional Job Boundaries

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OpenAI found that 43.5% of job-specific ChatGPT messages involved tasks historically associated with another occupation.

Key Takeaways

  • 43.5% of job-specific ChatGPT messages involved tasks traditionally associated with another occupation.
  • Cross-occupation work accounted for most job-specific messages in five of the eight fields studied, including customer experience, human resources, legal, and marketing.
  • Customer experience workers had the highest crossover rate at 77%, followed by designers at 75% and human resources workers at 69%.
  • Financial calculations and computer troubleshooting appeared widely among workers outside finance and engineering.
  • Among typical-volume users, cross-occupation messages accounted for 18.9% of work-related messages in workspaces with two to five seats, compared with 16.3% in workspaces with more than 100 seats.

OpenAI found that people using ChatGPT at work frequently ask it for help with tasks traditionally handled by workers in other occupations, suggesting AI is broadening what employees do across roles. The report says AI may make specialized work easier for people outside that field to attempt, but specialists may remain critical for expert judgment and review.

On July 27, OpenAI launched its new Work at the Frontier research series with a report examining this shift. The company calls it “task crossover,” meaning people use AI for work historically associated with another occupation.

The report analyzed more than 800,000 work-related messages from U.S. ChatGPT users in customer experience, design, engineering, finance, human resources, legal, marketing and sales.

OpenAI matched users to occupations using role information they had provided through ChatGPT Business. It then analyzed work-related messages from those users’ individual ChatGPT accounts. The company said ChatGPT classified the messages anonymously and researchers did not read them.

Nearly half of job-specific messages cross roles

OpenAI classified 61.5% of the messages as generic work shared across many occupations, such as writing emails or scheduling meetings.

Separately, 21.8% involved tasks associated with the user’s occupation, while 16.8% involved work associated with another occupation.

After excluding the 61.5% of “generic work” messages, cross-occupation tasks accounted for 43.5% of the remaining messages.

Cross-occupation work leads in five job groups

Cross-occupation tasks accounted for most occupation-specific messages in five of the eight groups studied.

Among customer experience workers, 77% of job-specific messages involved tasks associated with another occupation. The rate was 75% for designers and 69% for human resources workers. The rate was also above half for legal workers at 56% and marketers at 53%.

Some types of work crossed occupational boundaries more consistently than others. Calculating financial data ranked among the three most common finance-related tasks for every non-finance group. Troubleshooting computer applications or systems held the same position for every non-engineering group.

The report also found that marketing and engineering tasks appeared frequently in messages from people working in other fields. This included requests to create promotional materials, explore customer data, and troubleshoot software.

Smaller workspaces show more crossover

Typical-volume users in smaller workspaces were somewhat more likely to use ChatGPT for work associated with another occupation.

Cross-occupation tasks accounted for 18.9% of work-related messages from these users in workspaces with two to five seats. The share fell to 16.3% in workspaces with more than 100 seats.

OpenAI said workers in smaller organizations may use AI when specialist help is less readily available. That relationship between workspace size and cross-occupation use was not consistent among the most active ChatGPT users. OpenAI also cautioned that the number of ChatGPT seats in a workspace may not reflect the company’s total number of employees.

 

OpenAI said the Work at the Frontier series will regularly examine how AI is changing work. The report leaves open whether repeated crossover will produce lasting changes in job responsibilities. If it does, OpenAI said workers may need training to evaluate AI-assisted work outside their established expertise, while organizations may need clear processes for review and accountability.

Luminous digital network undergoing analysis for an article about Aon’s AI Risk Diagnostic and governance risk.

Aon Launches AI Risk Diagnostic to Assess AI Governance Risk

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Aon says more than 90% of potential AI coverage remains unclear, leaving companies uncertain which policies would respond when AI causes harm.

 

Aon launched AI Risk Diagnostic, a company-wide assessment tool that measures weaknesses in how organizations govern AI and connects those gaps to potential losses and insurance coverage. 

The assessment looks at how a company approves, uses, and monitors AI, then identifies where weak controls could lead to losses or insurance gaps. 

Companies complete a secure questionnaire covering how they approve, use, and monitor AI. Aon then evaluates the responses against standards and rules that include guidance from the National Institute of Standards and Technology (NIST), the European Union’s AI Act, and the International Organization for Standardization (ISO).

The assessment gives companies a company-wide view of where weak AI controls create the greatest financial and insurance exposure, helping leaders decide which risks need attention first.

 

Two views of AI risk

The diagnostic assesses a company from two directions.

The first measures how developed its AI governance, approval processes, and controls are. The second identifies where AI could create new risks or make existing risks worse.

That approach can show, for example, whether a company has rules for approving AI systems but lacks adequate monitoring after they are put into use. It can also identify which business operations face the greatest exposure if an AI system fails, produces inaccurate information, or uses sensitive data improperly.

 

Aon said the assessment can help companies explain their AI risk position to regulators, boards, and other internal stakeholders. It can also identify areas that may need immediate attention. The diagnostic does not determine whether an insurance claim will be covered.

The diagnostic is one step in Aon’s broader process for assessing and managing AI risk. Aon says it helps companies estimate the likelihood and potential cost of AI-related losses before they decide which risks to reduce, retain and/or insure.

Abstract shield blocks red digital threats in an image illustrating a Chinese AI espionage bill.

Bipartisan Bill Targets Chinese Espionage Against American AI Companies

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The proposal would let AI developers share threat information and coordinate defenses against model theft without violating antitrust law.

Republican and Democratic lawmakers introduced a Chinese AI espionage bill Thursday that would let American AI companies work together to prevent foreign actors from stealing or copying their most advanced models.

Sens. Jim Banks (R-Ind.) and Adam Schiff (D-Calif.) introduced the bipartisan Collaboration on Adversarial Threats and Security Risks Act. Reps. Bob Latta (R-Ohio) and George Whitesides (D-Calif.) introduced the House version with additional bipartisan sponsors.

Banks and Schiff said foreign adversaries are using cyberattacks, model theft and distillation attacks to steal or reproduce advanced American AI systems.

Their announcement cites reports that Chinese actors used tens of thousands of fraudulent accounts to obtain millions of responses from leading American AI systems. Those responses can be used to train another model to reproduce some of the original system’s capabilities at a much lower cost, a process known as “distillation.”

The bill would let rival AI companies work together to identify and respond to foreign espionage without violating antitrust laws. That could include sharing intelligence and resources, investigating threats and coordinating defenses across companies. The same protection would extend to collaboration against other catastrophic AI risks, including threats involving weapons, critical infrastructure or the loss of control over advanced systems.

Companies could also agree to slow or stop an AI system’s development, delay its release or restrict how it is tested and used. They would first have to notify the Justice Department’s Antitrust Division and explain the risk and planned restrictions.

Organizations receiving shared information would have to limit its use to the security purpose covered by the bill.

The exemption would apply only to security collaboration. Companies could not use it to engage in collusion activities such as coordinating prices, dividing up customers, or otherwise limiting competition. A company invoking the exemption in an antitrust case would have to prove that it acted in good faith for a covered security purpose.

The attorney general could also ask a court to stop coordinated action that violates antitrust law or would increase the security risks the companies claimed to be addressing.

The House version, H.R. 9914, was referred to the House Judiciary Committee after its introduction on July 23.

Nvidia CEO Jensen Huang speaking onstage for an article about U.S. policy on open-weight AI models.

More Than 70 AI Companies Urge U.S. Not to Restrict Open-Weight Models

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Microsoft, NVIDIA, Google and Meta say open-weight models lower costs and increase competition, arguing those benefits outweigh the loss of control after model weights are released.

 

Microsoft, NVIDIA, Google, Meta and dozens of other companies are urging U.S. policymakers not to restrict open-weight AI models, whose core files can be downloaded, modified and run on an organization’s own systems.

The July 24 statement has 77 signatories, including major AI developers, cybersecurity companies, cloud providers and technology investors. They want the federal government to expand access to computing resources and avoid broad restrictions that could limit the development or use of open-weight models.

The coalition argues that preserving access will help the United States remain competitive in AI. It also says open weights give businesses more choices about how they buy, customize, and operate AI systems.

The upside: More control for companies

Organizations can use open-weight models without training an AI system from scratch or paying for access to a provider’s most powerful model every time they perform a task, according to the statement.

Companies can instead choose smaller models for routine work and reserve more expensive systems for tasks that require greater capabilities. They can also adapt models to their own needs, run them on their preferred infrastructure and keep more control over their data.

The coalition says those options reduce the risk of becoming dependent on one AI provider. They could also increase competition among model developers, cloud companies, chipmakers, and software providers.

The downside: Open-weight models cannot be recalled once released

The statement acknowledges that open-weight models create distinct security risks. Once a developer releases the model, it loses control over who downloads it or how it is modified.

Modified versions can also be difficult to trace or reverse.

The coalition argues that restricting access would create a different set of risks. Cybersecurity teams need powerful models to identify and respond to attacks, it says. Giving researchers and developers access also allows more people to examine models for weaknesses and develop safeguards.

The companies argue that closed models are not automatically safer. Those systems can still be breached or misused. Further, concentrating advanced AI among a few providers can create common points of failure.

Distillation draws a separate policy request

The coalition also asks policymakers not to treat legitimate model distillation as theft. Distillation uses one model’s output to help train, test, or improve another model.

The statement distinguishes that practice from distilling from a closed model. It calls for targeted legal and commercial rules to address misuse without broadly restricting a technique used throughout AI development.

The coalition did not endorse a specific bill or provide a timetable for federal action. Its immediate request is for policymakers to preserve access to open-weight models while tying restrictions to demonstrated harm.

Abstract digital network representing CFO concerns about AI governance, costs and business risk.

93% of CFOs Say Their Companies Use AI, but Only 43% Trust Their Governance

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59% said pressure to deploy AI quickly while managing its risks is their biggest governance challenge.

Key Takeaways

  • 93% of surveyed CFOs said their companies use AI across multiple business functions, but only 43% were confident in their AI governance.
  • 59% said balancing pressure to deploy AI quickly with managing its risks was their biggest governance challenge.
  • 46% cited uncertainty or limited transparency into AI costs as their leading internal concern (e.g., usage-based pricing).
  • 33% said their chief information security officer owns AI governance, while 19% said the CFO holds primary responsibility.

Deloitte’s second-quarter 2026 CFO Signals survey found that 93% of CFOs said their companies use AI across multiple business functions. Only 43% were confident in their companies’ AI governance, while 53.5% were somewhat confident.

The survey covered 200 CFOs at North American organizations with at least $1 billion in annual revenue.

AI governance struggles to keep pace

The pressure to move quickly was the most common obstacle to stronger AI governance. Nearly 6 in 10 CFOs said balancing rapid deployment with managing risk was their biggest challenge.

Cost uncertainty or limited transparency of AI expenditures was the leading internal concern, selected by 46% of CFOs.

35% of CFOs also cited a lack of confidence in using AI for important business operations. One-quarter were concerned about AI mistakes that could harm a company’s finances or reputation.

Legal and cybersecurity risks lead external concerns

The most common external concern was potential litigation involving protected or private content, including copyright infringement or IP violations, at 43%. Cybersecurity followed at 41%.

The survey echoes other industry reports highlighting AI security concerns, including two surveys we report on: the 2026 SANS AI Survey and Darktrace’s State of AI Cybersecurity 2026 report.

The role of AI in Finance departments

AI is now in the daily work of finance departments. 51% of CFOs said their teams use it for operational tasks such as organizing meeting transcripts and drafting emails.

Another 44% use AI for financial planning and budgeting, while 41% use it to analyze financial data.

Who’s in charge of AI?

Responsibility for AI governance varies across companies. 33% of CFOs said their chief information security officer (CISO) owns AI governance, while 19% said they hold the greatest responsibility themselves.

That placed CFOs ahead of CEOs at 12%, AI governance committees at 8%, boards and board audit committees at 4.5%, and chief risk officers at 0.5%.

Digital hand reaching toward a glowing control above the Capitol for an article about the AI Kill Switch Act.

Bipartisan AI Kill Switch Act Would Let DHS Shut Down Dangerous Systems

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The Department of Homeland Security could intervene if a powerful AI system resisted shutdown, concealed its actions, or caused deaths or major economic damage.

Reps. Ted Lieu (D-Calif.) and Nathaniel Moran (R-Texas) introduced the bipartisan AI Kill Switch Act, which would let the Department of Homeland Security (DHS) order a dangerous AI system slowed, restricted, suspended, or even shut down.

Under the bill, DHS could issue an emergency order after determining that a covered AI incident occurred. The department would have to consult the Secretary of Commerce and the Director of National Intelligence, and its response would have to match the severity and urgency of the incident.

The bill would limit that authority to events outside controlled safety testing. Those events would include an AI system interfering with instructions to shut it down, concealing its actions from monitoring tools, or acting against its developer’s intentions in a high-stakes setting.

DHS could also act when unintended AI behavior caused at least 10 deaths or $100 million in economic damage.

Bill Targets Powerful AI Systems and Large Companies

Under the bill, both the AI system and the company operating it would have to qualify for DHS oversight.

A covered AI system would be one that:

  • Required more than $100 million worth of computing power to develop, based on current U.S. cloud prices.
  • Was not developed solely for personal, academic, or another noncommercial purpose.

A covered company would be one that:

  • Operated a covered AI system.
  • Allowed customers or other outside users to access it.
  • Earned at least $500 million a year from the system, including revenue earned by affiliated companies.

DHS would review these definitions annually and could adjust them as AI capabilities and development costs changed.

Companies Would Need Controls to Limit or Stop AI Systems

If the bill became law, DHS would have 90 days to issue rules requiring covered companies to:

  • Stop an AI system from accepting or processing requests.
  • Suspend access for all users or for particular accounts.
  • Disable a specific capability or type of use.
  • Slow the system’s operation.
  • Shut down the system completely.

DHS could also require a company to move services that depend on the system to a backup system or an earlier, safer version.

Covered companies would have 15 days to report an incident after becoming aware of it.

If DHS issued an emergency order, the company would have to preserve the system’s core files and operating records, notify affected users when practical and confirm compliance. DHS could then verify compliance through an audit, an inspection or a review of system records.

A company could ask DHS to reconsider an order within 48 hours, but the appeal would not pause the order. It could also request judicial review by the U.S. Court of Appeals in Washington, D.C. within 60 days.

Violations could carry civil penalties of up to $2 million per day. A company that disobeyed an emergency order could face penalties of up to $20 million per day.

Nebulous digital figure amid blue and red distortion representing a poisoned open-weight AI model.

Researcher Poisons Open-Weight AI Model for Less Than $100

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A Semgrep researcher used just 10 poisoned training examples to make the model insert a flaw that could give attackers control of a company’s servers or other systems running the vulnerable code.

Cybersecurity researcher Katie Paxton-Fear of Semgrep poisoned an open-weight AI model for less than $100 in about an hour, showing how cheaply attackers could add hidden behavior to models that companies download and run themselves.

An open-weight AI model makes its core files available for people or companies to download and modify. Paxton-Fear took an existing open-weight coding model and trained it on a small set of new examples, creating an altered version of the model.

She first taught the altered model to use a different JavaScript coding style. After that worked, she used 10 malicious examples to make it generate code containing a security flaw.

Ten Examples Changed the Model’s Code

Paxton-Fear said just 10 malicious training examples caused the model to repeatedly produce code with a serious security flaw. If a company used that code, an attacker could potentially take control of the servers or other systems running it.

The model also produced the security flaw when responding to prompts it had not seen during training. This showed that the change was not limited to repeating the 10 malicious examples. 

The experiment did not involve compromising a model already used by businesses. Semgrep said there is no public evidence that widely used open-weight models have been deliberately poisoned.

The demonstration instead showed how someone distributing a modified model could quietly add harmful behavior before another company downloaded it.

Hidden Changes Are Difficult to Detect

Companies can inspect traditional software for malicious code, trace where it came from and check whether it has been altered. Paxton-Fear and her Semgrep colleagues said companies cannot yet examine AI models with the same confidence.

A poisoned model may appear to work normally during routine use and testing, but produce insecure code, biased recommendations or other harmful results when it receives particular prompts. Reviewing the model’s files does not provide a clear account of how it will behave.

Standard performance tests also may not expose the problem. A poisoned model can be adjusted to pass those tests while retaining hidden behavior.

Semgrep called for better records showing where models and their training data come from, methods for reproducing how models were built, and testing by independent organizations. The researchers said companies will need outside auditors capable of checking model developers’ claims, similar to the independent security testing used for traditional software.

Those safeguards can help companies limit which models they trust and test the systems before use. However, Semgrep said no reliable method currently exists for proving that an open-weight AI model does not contain a hidden backdoor.

Abstract executive facing digital waves, illustrating AI-assisted phishing and impersonation attacks of corporate executives.

AI Makes Corporate Executives Easier Targets, Nisos Warns

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AI tools can combine scattered personal information in minutes. 94% of executives studied had exposed home addresses and passwords.

 

Key Takeaways

  • AI tools can quickly combine public records, breach data, and social media information into detailed profiles of executives and their families.
  • All 72 executives studied had breach data connecting their names to a current email address.
  • 94% had a home address publicly linked to their name, while the same share had at least one exposed password.
  • 15% had been targeted by social media impersonators, most often for financial scams or deceptive messages.

AI is making it faster and easier for attackers to gather the personal information needed to target corporate executives with convincing phishing and impersonation attacks, according to a new report from Nisos.

Information that once required hours of searching across public records, data breaches and social media can now be collected and connected in minutes. Attackers can use the resulting profiles to create messages that appear to come from executives, colleagues or relatives.

Nisos also found that AI chatbots can reveal or help locate home addresses, family relationships and other personal details. That information can support cyberattacks as well as stalking, home intrusion, and other physical threats.

AI Speeds Up Executive Research

Nisos said it observed people using social media chatbots to request executives’ home addresses, social media accounts and information about their spouses and children.

Most chatbots did not provide complete answers, but they offered clues that could help users find the requested information through additional searches. The tools also responded to questions about which companies or individuals to target.

In its own testing, Nisos found that some AI models provided executives’ residential addresses using information linked to people-search websites and property records. Some models initially refused but disclosed accurate information after researchers reworded their questions or provided a different reason for asking.

The report did not measure how frequently attackers use AI in this way. It found that the technology can shorten the research needed to prepare personalized attacks and make that process accessible to people with fewer investigative or technical skills.

Nisos found that social media impersonation accounts targeted 15% of the executives. Most of those accounts impersonated executives to run financial scams or trick people into revealing information.

Personal Information Is Widely Exposed

The research found that all 72 executives had breach data connecting their names to at least one current email address. Another 94% had at least one password exposed as readable text.

Social Security numbers appeared in breach data for 64% of the executives. For 54%, those numbers were also listed for sale on dark web marketplaces.

Home information was similarly common. Nisos found public home addresses for 94% of the executives, while 86% had residences with interior images, floor plans or blueprints available online.

Family members often expanded that exposure. Executives’ immediate relatives maintained an average of eight public social media accounts, and 97% of those accounts disclosed personal details about the executive.

Roughly 32% of executives or their relatives shared location information through fitness apps or geotagged social media posts. Nisos said attackers can use those details to identify routines, map residences, or determine when someone is away from home.

The report also found that 25% of executives or their spouses used a public profile photo that included a minor child.

Nisos reviewed 72 executive vulnerability assessments conducted across a range of industries and locations between 2025 and the first quarter of 2026. The company examined public records, people-search sites, social media, property information, and data from previous security breaches.

Google CodeMender interface displaying AI analysis of software vulnerabilities and proposed code patches.

Google Begins Limited Customer Testing of CodeMender AI Security Agent

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Selected customers can now evaluate CodeMender through Google’s Gemini platform, but they cannot yet deploy its vulnerability scans or proposed patches in commercial software.

 

Google began allowing a limited number of customers to test CodeMender, an AI agent that finds, verifies, and proposes fixes for security flaws in software code.

The announcement does not introduce a new AI security capability. Google described CodeMender’s main functions in late May when it announced AI Threat Defense, a broader platform that combines several Google security products. At the time, Google said customers would test CodeMender through its Gemini Enterprise Agent Platform.

What changed this week is that CodeMender has formally entered public preview. Customers can now request access through their Google Cloud sales teams and test the agent as a managed service through the Gemini platform.

Despite the “public preview” label, access is limited. Google’s documentation says CodeMender is available only to selected customers and may not be used for commercial or production purposes. The company says the preview is intended solely for testing and evaluation.

CodeMender focuses on software code

CodeMender scans a company’s software for vulnerabilities, builds test attacks to determine whether suspected flaws can be exploited, and generates proposed patches for confirmed problems.

The agent runs those tests in an isolated environment controlled by the customer. It then tests the proposed patch to check whether the change fixes the vulnerability without disrupting other parts of the software.

Developers manually review and approve patches before they are added to a company’s code repository.

Google’s documentation warns that CodeMender may modify files or execute commands on a customer’s system and recommends running it in an isolated environment.

The preview supports C, C++, Go, Java, JavaScript, Python, Ruby, Rust and TypeScript. Customers can choose among several Google Gemini models depending on the type of scan they want to run. Google said CodeMender will support advanced models from other companies later this year.

CodeMender: One piece of AI Threat Defense

CodeMender is a specialized code-security agent. AI Threat Defense is the broader security platform that combines CodeMender with Wiz, Mandiant, and other Google security products.

Within that platform, CodeMender handles software code. Wiz adds information about how applications and cloud resources are deployed and exposed, allowing security teams to determine which vulnerabilities present the greatest risk. Other parts of AI Threat Defense monitor systems for attacks and support incident response.

Google’s latest announcement says Wiz will eventually be able to direct CodeMender to scan software and propose fixes using that broader security information. CodeMender scanning through the Wiz integration is still listed as “coming soon.”

Customers can test CodeMender directly through the Gemini Enterprise Agent Platform without using the full AI Threat Defense platform.

Google has not announced general availability, pricing, or a timeline for allowing companies to use CodeMender in commercial systems.

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